Prescription opioid use among people with opioid dependence and concurrent benzodiazepine and gabapentinoid exposure: An analysis of overdose and all-cause mortality
Bibliographic record
Abstract
BACKGROUND: Studies investigating mortality risk associated with use of opioid analgesics, benzodiazepines, gabapentinoids, and opioid agonist treatment (OAT) among people with opioid dependence (PWOD) are lacking. This study addresses this gap using a cohort of 37,994 PWOD initiating opioid analgesics between July 2003 and July 2018 in New South Wales, Australia. METHODS: Linked administrative records provided data on dispensings, sociodemographics, clinical characteristics, OAT, and mortality. Cox proportional hazards models assessed associations between time-varying measures of individual and concurrent medicine use and OAT with all-cause mortality, accidental opioid overdose, non-drug induced accidents, and non-drug-induced suicide. Opioid analgesic dose effects, expressed as oral morphine equivalents (OMEs) per day, were also examined. OUTCOMES: During the study period, 3167 individuals died. Compared with no use, all medicines of interest were associated with increased accidental opioid overdose risk; hazard ratios (HR) ranged from 1.33 (95 % CI: 1.05-1.68) for opioid analgesic use to 6.10 (95 % CI: 4.11-9.06) for opioid analgesic, benzodiazepine and gabapentinoid use. Benzodiazepine use was associated with increased non-drug-induced accidents and non-drug-induced suicides. For all-cause mortality, all combinations of benzodiazepines and gabapentinoids with opioid analgesics were associated with increased risk (aHRs ranged from 1.35 to 2.73). For most medicines/medicine combinations, all-cause mortality risk was reduced when in OAT compared to out of OAT. Higher opioid analgesic doses were associated with increased all-cause mortality (e.g., 90-199 mg vs 1-49 mg OME per day: HR 1.90 [95 % CI: 1.52-2.40]). INTERPRETATION: The increased mortality risk associated with benzodiazepines and gabapentinoids among PWOD appear to be reduced when engaged in OAT. A greater focus on encouraging OAT engagement, providing overdose prevention education, and access and coverage of overdose antidotes is necessary to minimise the unintended consequences of medicines use in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".