Weather warnings predict fall-related injuries among older adults
Bibliographic record
Abstract
BACKGROUND: weather predictions are a useful tool for informing public health planning and prevention strategies for non-injury health outcomes, but the association between winter weather warnings and fall-related injuries has not been assessed previously. OBJECTIVE: to examine the association between fall-related injuries among older adults and government-issued winter weather warnings. METHODS: using a dynamic cohort of individuals ≥65 years of age who lived in Montreal between 1998 and 2006, we identified all fall-related injuries from administrative data using a validated set of diagnostic and procedure codes. We compared rates of injuries on days with freezing rain or snowstorm warnings to rates observed on days without warnings. We also compared the incidence of injuries on winter days to non-winter days. All analyses were performed overall and stratified by age and sex. RESULTS: freezing rain alerts were associated with an increase in fall-related injuries (incidence rate ratio [IRR] = 1.20, 95% confidence interval [CI]: 1.08-1.32), particularly among males (IRR = 1.31, 95% CI: 1.10-1.56), and lower rates of injuries were associated with snowstorm alerts (IRR = 0.89, 95% CI: 0.80-0.99). The rate of fall-related injuries did not differ seasonally (IRR = 1.00, 95% CI: 0.97-1.03). CONCLUSIONS: official weather warnings are predictive of increases in fall-related injuries among older adults. Public health agencies should consider using these warnings to trigger initiation of injury prevention strategies in advance of inclement weather.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".