Temporal trends and characteristics of fall-related deaths, hospitalizations and emergency department visits among older adults in Canada
Bibliographic record
Abstract
Falls among older adults (aged 65 years and older) are a public health concern in Canada. Fall-related injuries can cause a reduction in quality of life among older adults, and death. They also entail substantial health care costs. It is essential to monitor fallrelated injuries and deaths among older adults to better understand temporal trends and characteristics and to evaluate fall prevention strategies. We used the most up-to-date data from the Canadian Vital Statistics-Death database, Discharge Abstract Database and National Ambulatory Care Reporting System to analyze the temporal trends of fallrelated mortality, hospitalizations and emergency department (ED) visits among older adults in Canada over more than a decade. Age and sex characteristics were also examined. In 2022, 7189 older adults died due to a fall in Canada (excluding Yukon). From 2010 to 2022, deaths due to falls generally increased in both number and rates. In fiscal year 2023/24, there were 81 599 fall-related hospitalizations in Canada (excluding Quebec) and 212 570 fall-related ED visits in Ontario and Alberta. From fiscal year 2010/11 to 2023/24, even though the overall trend of the rates of fall-related hospitalizations and ED visits did not increase, the numbers generally rose year by year except in 2020/21, the early stage of the COVID-19 pandemic. As for the age and sex characteristics, the rates for deaths, hospitalizations and ED visits rose with advancing age for both men and women. With the aging population, continuous monitoring of the trends is crucial for fall prevention.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| 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".