Fall Risk–Increasing Drugs and Fall-Related Injuries among Older Adults in Ontario: A Population-Based Matched Case-Control Study
Bibliographic record
Abstract
OBJECTIVES: Commonly prescribed medications individually increase the risk of falls. Less is known about the association between multiple fall risk-increasing drug (FRID) use and falls. We examined the association between 12 major FRID classes, alone and in combination and fall-related injuries among older adults in home care (HC) and long-term care (LTC) settings. DESIGN: Matched, case-control study. SETTING AND PARTICIPANTS: HC recipients and LTC residents in Ontario, Canada, from 2008 to 2016. METHODS: Cases were matched to controls by sex, age, history of falls, calendar year, and disease risk score. Using multivariable logistic regression, the associations between FRID exposure in the 90 days preceding falls and fall-related injuries that required emergency department or hospitalization were determined with adjusted odds ratios (aORs) and 95% confidence intervals (CIs). RESULTS: Exposure to any FRID increased the risk of fall-related injury when compared with non-users in both HC (aOR, 1.34; 95% CI, 1.30-1.40) and LTC (aOR, 1.54; 95% CI, 1.46-1.63) populations. The increased odds of fall-related injuries were evident among most FRID categories, with the highest odds found with dopaminergic agents and antidepressants in both HC and LTC populations. The use of multiple FRIDs was associated with a greater odds of fall-related injury. Exposure to ≥5 FRIDs was associated with an almost twofold higher odds of fall-related injury in HC (aOR, 1.67; 95% CI, 1.57-1.77) and LTC (aOR, 1.92; 95% CI, 1.73-2.13) residents compared with non-users. The findings were similar across multiple subgroups and sensitivity analyses, with higher odds among new users compared with chronic users. CONCLUSIONS AND IMPLICATIONS: Multiple categories of FRIDs are associated with an increased risk of fall-related injuries in older adults. Clinicians should minimize use of these medications wherever possible. Fall prevention initiatives should incorporate strategies to prioritize deprescription of the highest risk FRIDs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".