The association between benzodiazepine co-prescription, opioid agonist treatment and mortality: a systematic review
Bibliographic record
Abstract
BACKGROUND: Opioid agonist treatment (OAT) is the preferred treatment for opioid dependence due to benefits such as treatment retention, reduced opioid use and mortality. Benzodiazepine co-dependence is common in OAT patients and has been linked to increased mortality. Prescribing benzodiazepines during OAT has been tried to reduce the harms of extra-medical benzodiazepine use. This systematic review examines association between benzodiazepine co-prescription during OAT and mortality. METHODS: We searched MEDLINE, Embase, Psych INFO, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials and Epistemonikos for reports published from database inception to June 2021. The searches were updated in February 2024. We included studies comparing mortality rates in OAT patients with and without benzodiazepine co-prescription. Two reviewers independently screened, extracted data, and assessed risk of bias from eligible studies with the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool. We combined the effect estimates in meta-analyses where possible. The certainty of the pooled effect estimates was assessed using the GRADE approach. RESULTS: We included six observational studies (N = 84,452) conducted in Sweden, Scotland, Canada, England, and the USA. Moderate-certainty evidence linked benzodiazepine prescription to higher all-cause mortality on OAT (HR 1.83; 95% CI 1.59 to 2.11). Moderate-certainty evidence associated benzodiazepine prescription with higher non-drug-induced mortality during OAT and the whole observation period (HR 1.73; 95% CI 1.33 to 2.25) and HR 2.02; 95% CI 1.29 to 3.18). Low-certainty evidence suggested an association with higher drug-induced mortality on OAT (HR 2.36; 95% CI 1.38 to 4.0). Very low-certainty evidence linked benzodiazepine prescription to higher all-cause and drug-induced mortality throughout the observation period (HR 1.49; 95% CI 1.02 to 2.18 and HR 2.19; 95% CI 0.80 to 6.0). CONCLUSIONS: There is probably an association between prescribed benzodiazepine use and higher risk of all-cause mortality (on OAT) and mortality due to non-drug-induced causes (on OAT and on and off OAT). Benzodiazepine prescription may also be associated with higher all-cause mortality (on and off OAT) and drug-induced mortality (on OAT and on and off-OAT), but this is highly uncertain due to methodological issues and possible confounding.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".