Benzodiazepine prescription in Ontario residents aged 65 and over: a population-based study from 1998 to 2013
Bibliographic record
Abstract
Background: Although commonly used in anxiety and insomnia, recent guidelines recommend caution when prescribing benzodiazepines in the elderly. Here we examined rates of benzodiazepine prescribing to older adults in Ontario, Canada from 1998 to 2013 and impact of legislation that made prescribing regulations more strict. Method: Annual benzodiazepine prescription rates for Ontario residents aged 65 and over were examined using the Ontario Drug Benefit database which captures all publicly funded prescriptions. Since most drugs, including benzodiazepines, are funded for residents aged ⩾65, data are essentially population-based. Weighted least squares regression methods were used to examine trends in prescribing rates (all benzodiazepines, anxiolytics, hypnotics, short- and long-acting drugs and individual drugs) from 1998 to 2013 for all Ontario residents aged ⩾65 and by sex and 5-year age bands. Impact on monthly prescribing rates of legislative changes (November 2011) which aimed to promote appropriate prescribing and dispensing practices for controlled substances, including requiring prescribers to record specified information, was assessed by constructing an interrupted time-series model. Results: Benzodiazepines were prescribed to 23.2% of the 1,412,638 Ontario residents aged ⩾65 in 1998, declining to 14.9% of 2,057,899 residents aged ⩾65 in 2013 ( p < 0.001 for trend). Rates were significantly greater throughout in older age bands ( p < 0.001) and 1.54–1.62 times greater in females than males ( p < 0.001). Lorazepam was the most prescribed benzodiazepine throughout, but rates declined from 11.4% in 1998 to 8.5% in 2013. Diazepam rates fell from 2.3% to 0.7%. However, clonazepam prescription rates increased until 2011, 1.7-fold overall. After the November 2011 legal changes, downward shifts were observed in total benzodiazepine prescription rates and for each drug individually. The step function, conditional on covariates, suggested benzodiazepine rates after November 2011 were 2.89 per 1000 ( p < 0.001) below rates observed previously, representing a relative reduction of 4.8% compared to the year before the intervention. Conclusion: Benzodiazepine prescribing rates declined markedly in this population from 1998 to 2013. Targeted legislation may have reduced rates, but the effect, although statistically significant, was small.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".