Use of benzodiazepines and related drugs in Manitoba: a population-based study
Bibliographic record
Abstract
BACKGROUND: Despite their favourable toxicology profile, benzodiazepines and the related Z-drugs (zopiclone, zolpidem and zaleplon) have been associated with physiological tolerance, dependence and addiction. Evidence of harm (e.g., falls, motor vehicle collisions and cognitive disturbances) has been reported in older populations. The aim of this study was to determine the relation between users' characteristics and the use of benzodiazepines and Z-drugs in Manitoba over a 16-year period. METHODS: This time-series analysis was based on prescription data from Apr. 1, 1996, to Mar. 31, 2012, obtained from the Drug Product Information Network database of Manitoba. We obtained sociodemographic information on benzodiazepine and Z-drug users from the Population Registry and determined changes in utilization rates over time using generalized estimating equations. RESULTS: Overall, the prevalence of benzodiazepine use remained stable at about 61.0 per 1000 population between 1996/97 and 2011/12; however, the prevalence of Z-drug use increased steadily from 10.9 to 37.0 per 1000 over the same period. In older people (≥ 65 years), the incidence of benzodiazepine use decreased from 55.5 to 30.3 users per 1000, whereas the incidence of Z-drug use increased from 7.3 to 20.3 users per 1000 over the study period. Among those 18-64 years of age, the incidence of benzodiazepine use decreased from 30.1 to 27.6 users per 1000, but the increase in incidence of Z-drug use was more than 2-fold. The youngest population (≤ 17 years) showed the lowest rates of use of these drugs. The highest rates of use were observed among older women and the low-income population. INTERPRETATION: Over the study period, benzodiazepines have been prescribed less frequently to older patients in Manitoba; however, zopiclone prescribing has continued to increase for all age groups. The reasons for this increase remain to be determined.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".