Differences in fall injury hospitalization and related survival rates among older adults across age, sex, and areas of residence in Canada
Bibliographic record
Abstract
BACKGROUND: Falls are the leading cause of injury-related hospital admissions in Canadian older adults, accounting for 85 % of injury hospitalizations among older adults aged over 65 years. While many of these injuries can lead to death, the survival rates of fall-related injuries are rarely examined. This surveillance study examined the fall injury hospitalization and survival rates among older adults in the context of place. METHODS: Saskatchewan's health administrative data on injury hospitalizations among individuals aged 65 years and over (n = 39,867) was utilized for this study. Variables of interest included age group, sex, and the geographical area of residence at the time of hospitalization (rural, urban, north). Logistic regression analysis was applied to determine the association of variables of interest (age group, sex, and area of residence at the time of hospitalization as the covariate) with frequency of fall injury hospitalizations. Probable time to death due to fall-related injury hospitalization was determined by survival analysis. RESULTS: Three key findings that emerged from the present study are the following: (1) fall injury hospitalizations accounted for 77 % of all injury hospitalizations; (2) fall injury hospitalization rates varied by age group, sex, and area of residence, with advancing age, women, and certain geographical areas showing higher rates; and (3) survival rates also varied by sex and area of residence. Women had longer survival estimates after a fall injury hospitalization compared to men, and those living in the north have the shortest survival estimates. CONCLUSIONS: The findings from the study highlighted the high rate of fall-related injury hospitalization among older adults varying with their age group, sex, and area of residence. These factors need to be considered in injury surveillance and fall prevention research as well as programs and policies that support the reduction of falls.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".