Injectable Opioid Agonist Treatment for Patients With Opioid Use Disorder: An Updated Review of Clinical and Cost-Effectiveness
Bibliographic record
Abstract
Main Take-Away Injectable opioid agonist treatment (iOAT) may be a promising option for individuals with opioid use disorder (OUD), particularly those whose disease has not responded to oral treatment. The evidence suggests benefits in treatment retention, reductions in illicit opioid and cocaine use, fewer interactions with the criminal justice system, and a potentially acceptable safety profile. What Is the Issue? OUD is a chronic, relapsing medical condition defined by a problematic pattern of opioid use over a 12-month period that leads to substantial impairment or distress. This disorder is characterized by symptoms such as a loss of control over use, craving, tolerance, withdrawal, and continued use of opioids despite negative social, occupational, or health consequences. Decision-makers are interested in understanding the clinical effectiveness, safety, and cost-effectiveness of iOAT for patients with OUD. What Did We Do? We searched key resources, including journal citation databases, and conducted a focused internet search for relevant evidence published since 2020. What Did We Find? iOAT with or without an oral opioid agonist may be more effective than other therapies (e.g., oral opioid agonist alone) or no therapy in improving treatment retention, reducing illicit opioid and cocaine use, and decreasing interactions with the criminal justice system. Injectable hydromorphone administered under medical supervision showed a good safety profile, with only mild adverse events reported and no significant differences compared to placebo. An economic evaluation from Australia found that a model prioritizing unsupervised iOAT with a smaller proportion of patients receiving supervised iOAT may be cost-effective. Scenarios focused more heavily on supervised iOAT were not cost-effective in that context. What Does This Mean? iOAT may be a promising treatment option for individuals with OUD, particularly for those whose disease has not responded to oral opioid agonist treatment. The evidence suggests benefits in treatment retention, reductions in illicit opioid and cocaine drug use, less involvement with the criminal justice system, and a potentially acceptable safety profile. Policy-makers may consider models of care that incorporate unsupervised or take-home iOAT to expand access to clinically effective and cost-effective care. Limitations in the evidence — such as incomplete outcome reporting, small sample sizes, lack of formal certainty assessments, and limited generalizability to health care systems in Canada — should be considered when making decisions.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".