The effect of opioid analgesics, benzodiazepines, gabapentinoids, and opioid agonist treatment on mortality risk among opioid-dependent people.
Bibliographic record
Abstract
BackgroundAs dispensings for benzodiazepines and gabapentinoids have increased in recent years, risk of increased mortality has been identified, particularly when used with opioids. There is limited research examining use of these medicines among people with opioid dependence and whether mortality risk varies according to opioid agonist treatment (OAT) status. This study characterizes patterns of opioid analgesic utilization and concomitant use of benzodiazepines, gabapentinoids and OAT among people with opioid dependence initiating opioid analgesics. It also assesses mortality risk associated with exposure to these medicines. MethodsRetrospective cohort study in New South Wales, Australia, including 28,891 people with documented opioid dependence initiating opioid analgesics between July 2003 and December 2018. Linked administrative records provided data on prescription dispensings, sociodemographics, clinical characteristics, OAT and mortality. Generalised estimating equation models estimated incidence rate ratios (IRR) comparing periods in and out of OAT for the number of opioid analgesic dispensings. Periods of concomitant use of opioid analgesics, benzodiazepines, gabapentinoids, and OAT were identified. Cox models assessed associations between concomitant medicines use with mortality risk. ResultsAt the time of opioid analgesic initiation, 43.7% of the cohort were in OAT. The most commonly initiated opioid was codeine (67.8%). In the 90 days prior to the index opioid dispensing, benzodiazepines were more frequently dispensed than gabapentinoids, but rates varied over time. Between 2004 and 2018, benzodiazepine dispensings decreased (41.7% to 21.1%) while gabapentinoid dispensings increased (0.2% to 7.9%). Incidence of opioid analgesic dispensings was higher during periods out of OAT compared to in OAT (5.8 v. 2.3 per person-year; IRR 0.39, 95% CI 0.38, 0.41). Analyses investigating associations between medicine exposure and mortality are ongoing. ConclusionPeople with opioid dependence had high rates of recent benzodiazepine utilization and current OAT enrollment at the time of opioid analgesic initiation. OAT was associated with a significant reduction in opioid analgesic prescribing.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".