A population-based case-control analysis of risk factors associated with mortality during the 2021 western North American heat dome: focus on chronic conditions and social vulnerability
Bibliographic record
Abstract
Abstract Western North America experienced an unprecedented extreme heat event (EHE) in early summer 2021. In the province of British Columbia (BC), this event was associated with an estimated 740 excess deaths, making it one of the deadliest weather events in Canadian history. This study uses a population-based case-control design to compare 1597 adults (cases) who died during the EHE (25 June–2 July 2021) with 7968 similar adults (controls) who survived. The objective was to identify risk factors for death during the EHE by examining differences in chronic diseases and social vulnerability between the cases and controls. We used care setting, age category, sex, and geographic area of cases to identify comparable surviving controls. We used logistic regression to estimate the odds ratio (OR) for each chronic disease, adjusted for care setting, age category, sex, and geographic area. We further adjusted for individual-level low-income status to identify changes in the estimated ORs with the addition of this indicator of social vulnerability. The risk factor most strongly associated with EHE mortality was individual-level low income. The fully adjusted OR [95% confidence interval] for receiving income assistance was 2.42 [1.98, 2.95]. The chronic disease most strongly associated with EHE mortality was schizophrenia, with a fully adjusted OR of 1.93 [1.51, 2.45]. Chronic obstructive pulmonary disease, parkinsonism, heart failure, chronic kidney disease, ischemic stroke, and substance use disorder were also associated with significantly higher odds of EHE mortality. These results confirm the roles of social vulnerability, mental illness, and other specific underlying chronic conditions (renal, respiratory, cardiovascular, cerebrovascular, and neurological) in risk of mortality during EHEs. This information is being used to inform policy and planning to reduce risk during future EHEs in BC and across 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".