Global trends in the consumption of benzodiazepines and Z-drugs in 67 countries and regions from 2008 to 2018: a sales data analysis
Bibliographic record
Abstract
STUDY OBJECTIVES: To investigate the trends in the consumption of benzodiazepines (BZDs) and Z-drugs at global, regional, and national levels from 2008 to 2018, across 67 countries and regions. METHODS: This cross-sectional descriptive study investigated the consumption of BZDs and Z-drugs analyzed by global pharmaceutical sales data from the IQVIA-Multinational Integrated Data Analysis System database between 2008 and 2018. Consumption was measured in defined daily dose (DDD) per 1000 inhabitants per day (DDD/TID). The global, regional, and national trends were estimated using linear mixed models. Additional analyses were conducted by grouping countries by income level. The association between consumption and Gross Domestic Product (GDP) and the prevalence of different medical conditions was explored in univariable linear models. RESULTS: BZD consumption decreased annually by -1.88% (95% CI: -2.27%, -1.48%), and Z-drugs increased by + 3.28% (+2.55%, +4.01%). In 2008, the top ten countries for BZD and Z-drug consumption were all European, ranging from 63.69 to 128.24 DDD/TID. Very low levels were found in Russia, Kuwait, United Arab Emirates, Saudi Arabia, French West Africa, and the Philippines, with DDD/TID < 1. The consumption in high-income countries was much higher than in middle-income countries. The results showed that increased consumption of BZDs and Z-drugs was statistically associated (p < 0.05) with higher GDP and increased prevalence of anxiety, self-harm, neurological disorders, chronic respiratory diseases, cardiovascular diseases, and cancers. CONCLUSIONS: Distinct differences in consumption and trends of BZDs and Z-drugs were found across different countries and regions. Further exploration is needed to understand the association and safety of the use of BZDs and Z-drugs in patients with comorbidities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".