Changes in benzodiazepine, z-drug, and other sedative prescribing in primary care in Ireland between 2014 and 2022
Bibliographic record
Abstract
BACKGROUND: The trends in sedative use have varied in recent years. Benzodiazepines and z-drugs are indicated for anxiety and/or sleep disorders but should be limited to short-term use. The aim of this study is to examine trends and patterns in sedative prescribing in Ireland between 2014 and 2022, as well as comparing trends between Ireland and England within the same period. METHODS: Monthly data on medicines prescribed and dispensed in primary care on the means-tested General Medical Services (GMS) scheme in Ireland were used. Volumes of prescribed benzodiazepine and z-drug use and patterns of prescribing, including initiations, discontinuations, chronic use, and high-risk prescribing were summarized per year. Other sedating agents (sedating antihistamines, antidepressants, and antipsychotics) were also analysed. Volume of use outcomes were compared with NHS data from England for the same period. RESULTS: The rate of benzodiazepine and z-drug dispensings per 1000 GMS population decreased by 5%, from 1531 in 2014 to 1474 in 2022. By comparison in England, there was a steeper decrease of 27% in the dispensing rate and the level of use was substantially lower, falling from 288 dispensings per 1000 population in 2014 to 210 in 2022. In Ireland, dispensing rates were highest amongst women and older age groups. High-risk dispensings of benzodiazepines and z-drugs decreased over the study period. DISCUSSION: Despite decreases in benzodiazepine and z-drug dispensings, rates remain high in Ireland and may suggest a need for enhanced availability of non-pharmacological interventions, and improved education and deprescribing support for healthcare professionals.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".