Concurrent use of opioids and benzodiazepines/Z-drugs in Alberta, Canada and the risk of hospitalisation and death: a case cross-over study
Bibliographic record
Abstract
OBJECTIVES: Coprescribing of benzodiazepines/Z-drugs (BZDs) and opioids is a drug-use pattern of considerable concern due to risk of adverse events. The objective of this study is to estimate the effect of concurrent use of BZDs on the risk of hospitalisations/emergency department (ED) visits and deaths among opioid users. DESIGN, SETTING AND PARTICIPANTS: We conducted a population-based case cross-over study during 2016-2018 involving Albertans 18 years of age and over who received opioids. From this group, we identified 1 056 773 people who were hospitalised or visited the ED, and 31 998 who died. INTERVENTION: Concurrent use of opioids and BZDs. OUTCOMES: We estimated the risk of incident all-cause hospitalisation/ED visits and all-cause mortality associated with concurrent BZD use by applying a matched-pair analyses comparing concurrent use to opioid only use. RESULTS: Concurrent BZD use occurred in 17% of opioid users (179 805/1 056 773). Overall, concurrent use was associated with higher risk of hospitalisation/ED visit (OR 1.13, p<0.001) and all cause death (OR 1.90; p<0.001). The estimated risk of hospitalisation/ED visit was highest in those >65 (OR 1.5; p<0.001), using multiple health providers (OR 1.67; p<0.001) and >365 days of opioid use (OR 1.76; p<0.001). Events due to opioid toxicity were also associated with concurrent use (OR 1.8; p<0.001). Opioid dose-response effects among concurrent patients who died were also noted (OR 3.13; p<0.001). INTERPRETATION: Concurrent use of opioids and BZDs further contributes to the risk of hospitalisation/ED visits and mortality in Alberta, Canada over opioid use alone, with higher opioid doses, older age and increased number of unique health providers carrying higher risks. Regulatory bodies and health providers should reinforce safe drug-use practices and be vigilant about coprescribing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".