Chronic Diseases Associated With Mortality in British Columbia, Canada During the 2021 Western North America Extreme Heat Event
Bibliographic record
Abstract
Western North America experienced an unprecedented extreme heat event (EHE) in 2021, characterized by high temperatures and reduced air quality. There were approximately 740 excess deaths during the EHE in the province of British Columbia, making it one of the deadliest weather events in Canadian history. It is important to understand who is at risk of death during EHEs so that appropriate public health interventions can be developed. This study compares 1,614 deaths from 25 June to 02 July 2021 with 6,524 deaths on the same dates from 2012 to 2020 to examine differences in the prevalence of 26 chronic diseases between the two groups. Conditional logistic regression was used to estimate the odds ratio (OR) for each chronic disease, adjusted for age, sex, and all other diseases, and conditioned on geographic area. The OR [95% confidence interval] for schizophrenia among all EHE deaths was 3.07 [2.39, 3.94], and was larger than the ORs for other conditions. Chronic kidney disease and ischemic heart disease were also significantly increased among all EHE deaths, with ORs of 1.36 [1.18, 1.56] and 1.18 [1.00, 1.38], respectively. Chronic diseases associated with EHE mortality were somewhat different for deaths attributed to extreme heat, deaths with an unknown/pending cause, and non-heat-related deaths. Schizophrenia was the only condition associated with significantly increased odds of EHE mortality in all three subgroups. These results confirm the role of mental illness in EHE risk and provide further impetus for interventions that target specific groups of high-risk individuals based on underlying chronic conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".