Impact of Extreme Weather Events and Climate Variability on the Health of Canadians: A Multicenter Study
Bibliographic record
Abstract
SAA4-PD-04 Relatively small changes in the average climate conditions could produce large changes in the frequency and magnitude of weather factors and events. In order to asses the effects of climate change and climate variation on the health of a population, it is necessary to understand the relationship between health and climate under current and past conditions. It is important to identify and quantify the health effects associate with climate and how these effects may vary by region or by population. The purpose of this project is to assess the prevalence of illness, injury and death as a result of extreme heat and cold events through the collection and evaluation of administrative health data in the form of mortality, hospital separations, and emergency department records from selected urban cities across Canada (Vancouver, Winnipeg, Edmonton, Ottawa, Quebec City, and Halifax). The synoptic classification system by Environment Canada assigns each day in terms of the various weather variables into a particular synoptic category based primarily on air mass differentiation. Among the selected cities, Ottawa possessed the highest percentage of hot weather group (8%), followed by Quebec City (5.8%) and Winnipeg (3.6%), whereas Vancouver had the least number of days in the hot weather group (0.6%). The cold weather group occurs most frequently in Winnipeg (16.9%) and least frequently in Vancouver (6.9%). The percentage of air pollution days ranged from 46.6% (Quebec City) to 53% (Halifax). Elevated mortality and hospital separations are present when daily mortality exceeds the baseline and are associated with extreme temperatures and acute exposures to air pollution. This pattern is consistent for all cities investigated. In addition to annual total counts, daily mean elevated mortality and hospital separations breakdown by weather groups were also examine for the 6 cities. Daily elevated mortality was much higher for the extreme temperatures and air pollution-related groups than it was for “other” (comfortable) weather groups. Linking the regional/area health data to synoptic weather classifications of extreme heat and cols events over an approximate 10-year period (1990–2002) will provide new knowledge regarding the vulnerability of certain populations and/or regions and establish the need for a surveillance system to monitor associated health impacts of climate variability. These results can provide more accurate assessments of the health effects of climate change in Canada, a base measure for health service utilization during these extreme weather events and a scientific basis for preventive and adaptation measures needed to policy and decision-makers. This is a joint project between Health Canada, Environment Canada, participating hospitals, and universities across Canada, partially funded by the Climate Change Adaptation Initiative, Natural Resources Canada.
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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 | 0.004 | 0.001 |
| 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".