Healthcare resource use and costs associated with extreme heat in Alberta, Canada
Bibliographic record
Abstract
Heat events are a growing public health concern. There is an opportunity to better characterize how heat events affect healthcare system utilization. We evaluated the heat-related healthcare resource use and costs in Alberta, Canada in the summer of 2021 when record-breaking extreme heat events occurred in the province. We conducted a population-based cohort study using Alberta administrative health data from May to September 2021 to identify and describe patients who used heat-related healthcare resources over this period. Costs were quantified and reported in Canadian dollars (CA$) using 2023 values. 4194 patients used heat-related healthcare resources, including 109 hospitalizations, 1020 ambulatory care visits (99.7% were ED visits), 310 ambulance transfers, and 5555 practitioner claims. Total heat-related healthcare costs were CA$3.2 million. Female sex, age, and a history of myocardial infarction, heart failure, dementia, or diabetes were found to be significantly associated with increased use of heat-related healthcare resources. History of cardiovascular disease (27.1%) or diabetes (12%) were more frequent in patients hospitalized or attended ED (30 and 14.2%, respectively) compared to those who only used outpatient or physician services (26.3 and 11.2%, respectively; all p < 0.05). Heat-related healthcare resource use and costs during the summer of 2021 in Alberta was substantial. Females, older persons, and people with a history of cardiovascular disease were the most affected. This is likely an underestimation of the overall heat impact. Additional research is needed to quantify the broader impact of extreme heat events on the healthcare system and on society.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".