Benzodiazepine Use in Older Adults in the United States, Ontario, and Australia from 2010 to 2016
Bibliographic record
Abstract
OBJECTIVES: To detail annual trends in benzodiazepine incidence and prevalence in older adults between 2010 and 2016 in three countries. DESIGN: Observational multicountry cohort study with harmonized study protocol. SETTING: The United States (veteran population); Ontario, Canada; and Australia. PARTICIPANTS: All people aged 65 and older (8,270,000 people). MEASUREMENTS: Annual incidence and prevalence of benzodiazepine use stratified according to age group (65-74, 75-84, ≥85) and sex. We performed multiple regression analyses to assess whether rates of incident and prevalent use changed significantly over time. RESULTS: Over the study period, we observed a significant decrease in incident benzodiazepine use in the United States (2.6% to 1.7%) and Ontario (6.0% to 4.4%) but not Australia (7.0% to 6.7%). We found significant declines in prevalent use in all countries (United States: 9.2% to 7.3%; Ontario: 18.2% to 13.4%; Australia: 20.2% to 16.8%). Although incidence and prevalence increased with age in Ontario and Australia, they decreased with age in the United States. Incidence and prevalence were higher in women in all countries. CONCLUSION: Consistent with other international studies, there have been small but significant reductions in the incidence and prevalence of benzodiazepine use in older adults in all three countries, with the exception of incidence in Australia, although use remains inappropriately high-particularly in those aged 85 and older-which warrants further attention from clinicians and policy-makers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".