The Impact of Extreme Heat Events on Emergency Departments in Canadian Hospitals
Bibliographic record
Abstract
INTRODUCTION: Mean daily temperatures in Canada rose 1.7°C between 1948 and 2016, and the frequency, severity, and duration of extreme heat events has increased. These events can exacerbate underlying health conditions, bringing patients to emergency departments (EDs). This retrospective analysis assessed the impact of temperature and humidex on ED volume and length of stay (LOS). METHODS: LOS is an indicator of ED overcrowding and system performance. Using daily maximum temperatures and humidex values, this study investigated the impact of mean 3-d temperatures and humidex preceding ED presentation on the median and maximum ED LOS and patient volume in 2 community hospitals in Montreal, Quebec, during the summer months of 2016 to 2018. Data were analyzed with 1-way analysis of variance with post hoc Fisher least significant difference tests and Spearman correlation tests. RESULTS: The mean maximum temperature and humidex were 26.1°C and 30.4°C, respectively (n=276 d). Mean 3-d temperatures ≥30°C were associated with higher daily ED volumes in both hospitals (138 vs 121, P=0.002 and 132 vs 125, P=0.03) and with increased median LOS at 1 hospital (8.9 vs 7.6 h, P=0.03). Mean 3-d humidex ≥35 was associated with higher daily ED volumes at both hospitals as well (136 vs 123, P=0.01 and 133 vs 125, P=0.009) with an increased median LOS at 1 hospital (8.6 vs 6.9 h, P=0.0001) with humidex values of 25 to 29.9°C. CONCLUSIONS: Heat events were associated with increased ED presentations and LOS. This study suggests that a warming climate can impede emergency service provision by increasing the demand for and delaying timely care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".