Polypharmacy as a Simple Measure for Assessing the Risk of Fall-Related Hospitalization in Older Adults
Bibliographic record
Abstract
INTRODUCTION: Medication is a modifiable risk factor for falls. Evaluating medication use may aid in identifying individuals at increased risk of a first fall and in preventing associated outcomes. The aim of this study was to identify the best medication-based measure to evaluate fall-related hospitalization risk among older adults. METHODS: A population-based cohort was created using the Quebec Integrated Chronic Disease Surveillance System. Individuals aged >66 on April 1, 2019 (index date) insured by the public drug plan and without fall-related hospitalization the prior year were included. Five medication-based measures were developed, derived from the average number of medications claimed in the previous year (≥5, ≥10 medications, ≥5 third-level Anatomical Therapeutic Chemical classes, ≥1 fall-risk increasing drugs, ≥1 potentially inappropriate medications). Hazard ratios were estimated with sex-stratified Cox models to predict fall-related hospitalization in the year after index date. Predictive performances were compared between each medication-based measure. RESULTS: The cohort included 647,795 women and 529,725 men. Hazard ratios ranged from 1.43 (95% CI=1.37, 1.49) for ≥1 potentially inappropriate medication (women) to 2.17 (95% CI=2.05, 2.31) for ≥10 medications (men). Lowest predictive performance was for ≥1 potentially inappropriate medication (c-statistic=0.724 [women]; 0.722 [men]), and highest predictive performance was for ≥1 fall-risk increasing drug (c-statistic=0.735 [women], 0.736 [men]). CONCLUSIONS: All medication-based measures have similar performance in assessing fall-related hospitalization. From a public health standpoint, measures that have the added benefits of being simple and accessible, such as polypharmacy, can support surveillance efforts and inform public health actions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".