SHIFTS IN MORTALITY DURING A HOT WEATHER EVENT IN VANCOUVER, CANADA: RAPID ASSESSMENT USING CASE-ONLY ANALYSIS
Bibliographic record
Abstract
Background: During the summer of 2009, greater Vancouver (in British Columbia, Canada) experienced several days of unusually hot temperatures. Episodes of extreme heat in other northern cities have been associated with considerable excess mortality, especially among the most elderly, and those living in dense, and/or disadvantaged urban neighbourhoods. Methods: A case-only analysis was used to compare characteristics of individuals who died during the hottest week of 2009 with those who died (1) during earlier summer weeks in 2009 and (2) during the same calendar weeks in the summers of 2004-2008. Results: Risk of mortality during the hot weather event was significantly higher in the 65-74 age category than in the 85+ category (OR = 1.50; 95%CI = 1.09–2.06). Deaths at home were increased over deaths in hospitals or institutions (OR = 1.39; 95%CI = 1.06– 1.80). Densely populated administrative health areas were more affected. Conclusions: A significant shift towards deaths at home suggests that home-based protective measures should be part of planning for hot weather in Vancouver. Targeting should be considered for persons in the 65-74 age category. The case-only approach is quick and easy to apply, and it can provide authorities with useful information about patterns of mortality during localized, time limited events
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| 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 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".