Associations between meteorological factors and emergency department visits for unintentional falls during Ontario winters
Bibliographic record
Abstract
INTRODUCTION: Unintentional falls are a leading cause of injury-related hospital visits among Canadians, especially seniors. While certain meteorological conditions are suspected risk factors for fall-related injuries, few studies have quantified these associations across a wider range of age groups and with population-based datasets. METHODS: We applied a time-stratified case-crossover study design to characterize associations of highly-spatially-resolved meteorological factors and emergency department (ED) visits for falls, in Ontario, among those aged 5 years and older during the winter months (November to March) between 2011 and 2015. Conditional logistic models were used to estimate the odds ratios (ORs) and their 95% confidence intervals (CIs) for these visits in relation to daily snowfall accumulation, including single-day lags of up to one week before the visit, and daily mean temperature on the day of the visit. Analyses were stratified by age and sex. RESULTS: We identified 761 853 fall-related ED visits. The odds for these visits was increased for most days up to a week after a snowfall of 0.2 cm or greater (OR = 1.05-1.08) compared to days with no snowfall. This association was strongest among adults aged 30 to 64 years (OR = 1.16-1.19). The OR for fall-related ED visits on cold days (less than -9.4 °C) was reduced by 0.05 relative to days with an average daily temperature of 3.0 °C or higher (OR = 0.95; 95% CI: 0.94, 0.96), and this pattern was evident across all ages. There were no substantive differences in the strength of this association by sex. CONCLUSION: Snowfall and warmer winter temperatures were associated with an increased risk of fall-related ED visits during Ontario winters. These findings are relevant for developing falls prevention strategies and ensuring timely treatment.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".