Progressive Trends in the Prevalence of Benzodiazepine Prescribing in Older People in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: The extensive use of benzodiazepines has been a concern of healthcare providers and policy makers in Canada and around the world. The purpose of this study was to examine temporal trends in benzodiazepine prescriptions dispensed in older people from 1993-1998. DESIGN: Retrospective population-based cross-sectional study using administrative databases. SETTING: Ontario, Canada. PARTICIPANTS: The over 1 million residents of Ontario age 65 and older covered by the provincial universal drug benefit program. MEASUREMENTS: The main outcome measures were the prevalence, overall and with respect to age and gender, of benzodiazepine prescriptions dispensed and the ratio of the number of people to whom short- versus long-acting benzodiazepine prescriptions were dispensed in each study year. The annual rates of switching to other psychotropic agents were examined for those patients that discontinued filling benzodiazepine prescriptions. RESULTS: The annual prevalence of benzodiazepine prescriptions dispensed decreased consistently over time (25.1% in 1993 to 22.5% in 1998; P < .001). Benzodiazepine dispensing prevalence increased with increasing age (approximately 20% of those age 65 to 69 to approximately 30% of those age > or =85; P < .001) and more females than males received the medication (relative risk = 1.50, 95% confidence interval = 1.49-1.51). The ratio of short- to long-acting benzodiazepine prescriptions filled increased over time (3.6 in 1993 to 5.8 in 1998; P < .001), in line with guideline recommendations. Rates of switching to antidepressants increased over time (8.5% in 1993 to 10.2% in 1998; P < .001), whereas switching to barbiturates was consistently low (0.12%; P = .403). CONCLUSION: The prevalence of benzodiazepine therapy for older people in Ontario has steadily declined between 1993 and 1998. There is a trend of dispensing relatively more short-acting than long-acting benzodiazepines and of replacing benzodiazepines with antidepressants in older people without a remarkable increase in barbiturate consumption. These findings suggest that, without undue regulation, physicians are making progress in the prescribing of benzodiazepine therapy on the basis of current knowledge available.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".