Benzodiazepine and opioid co-usage in the US population, 1999–2014: an exploratory analysis
Bibliographic record
Abstract
STUDY OBJECTIVES: The study objectives were to explore trends in prevalence of couse of benzodiazepine receptor modulators and opioids, and nonselective and selective (i.e. Z-drugs) benzodiazepine receptor modulators, in the United States, as well as risk factors for these drug utilization patterns. METHODS: This was a multiyear, cross-sectional, population-level study, using US health survey data. Data from eight National Health and Nutrition Examination Survey (NHANES) cycles were analyzed, from 1999-2000 until 2013-2014, with each survey cycle containing information on ~10 000 individuals. The main measure was prevalent prescription drug use within 30 days preceding survey administration. Drug usage was objectively confirmed for a large majority of participants though direct inspection of prescription bottles. RESULTS: The estimated prevalence of concurrent benzodiazepine receptor modulator and opioid use in the United States was 0.39% in 1999-2000 and 1.36% in 2013-2014, reflecting absolute and relative changes of +0.97% and +249%. The estimated prevalence of nonselective and selective benzodiazepine receptor modulator couse steadily rose in the United States from 0.05% in 1999-2000 to 0.47% in 2013-2014, reflecting absolute and relative increases of +0.42% and +840%. Independent risk factors for these two forms of psychoactive medication polypharmacy were identified. CONCLUSIONS: In this exploratory analysis, concurrent use of benzodiazepine receptor modulators and opioids, and nonselective and selective benzodiazepine receptor modulators, was found to have progressively risen in the United States. The progressive increases in these two forms of psychoactive medication polypharmacy are concerning, given that these drug use patterns are associated with increased risk for serious adverse outcomes.
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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) | 0.002 | 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 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".