Trends in falls among older adults before and during the COVID-19 pandemic in Ontario, Canada: A retrospective observational study
Bibliographic record
Abstract
BACKGROUND: The public health measures associated with the COVID-19 pandemic may have indirectly impacted other health outcomes, such as falls among older adults. The purpose of this study was to examine trends in fall-related hospitalizations and emergency department visits among older adults before and during the COVID-19 pandemic in Ontario, Canada. METHODS: We obtained fall-related hospitalizations (N = 301,945) and emergency department visit (N = 1,150,829) data from the Canadian Institute for Health Information databases from 2015 to 2022 for adults ages 65 and older in Ontario. Fall-related injuries were obtained using International Classification of Diseases, 10th edition, Canada codes. An interrupted time series analysis was used to model the change in weekly fall-related hospitalizations and emergency department visits before (January 6, 2015-March 16, 2020) and during (March 17, 2020-December 26, 2022) the pandemic. RESULTS: After adjusting for seasonality and population changes, an 8% decrease in fall-related hospitalizations [Relative Rate (RR) = 0.92, 95% Confidence Interval (CI): 0.85, 1.00] and a 23% decrease in fall-related emergency department visits (RR = 0.77, 95%CI: 0.59, 1.00) were observed immediately following the onset of the pandemic, followed by increasing trends during the pandemic for both outcomes. CONCLUSIONS: Following an abrupt decrease in hospitalizations and emergency department visits immediately following the onset of the pandemic, fall-related hospitalizations and emergency department visits have been increasing steadily and are approaching pre-pandemic levels. Further research exploring the factors contributing to these trends may inform future policies for public health emergencies that balance limiting the spread of disease among this population while supporting the physical, psychological, and social needs of this vulnerable group.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".