Effectiveness of Interventions for Prevention of Common Infections Among Opioid Users: A Systematic Review of Systematic Reviews
Bibliographic record
Abstract
Background: The North American opioid crisis is marked by high opioid-related mortality and morbidity, including opioid use-associated infections (OUAIs). Users of pharmaceutical and non-pharmaceutical opioids are at an increased risk of acquiring hepatitis C (HCV), human immunodeficiency virus (HIV), and other infections. No high-level evidence, however, has been synthesized regarding effectiveness of interventions to prevent OUAIs in legal, and illegal/mixed opioid users. The aim of the study is to synthesize available systematic review (SR)-level evidence on the scope and effectiveness of interventions to prevent OUAIs among opioid users. Methods: A SR of SRs approach was applied. We searched PubMed, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Epistemonikos and Google Scholar from inception to September 2020. Data selection and extraction were performed independently by three researchers. Risk of bias and quality of evidence were assessed using the AMSTAR2 tool. Results were narratively synthesized. Strength of evidence for each category was reported. Results: Eleven of twelve identified SRs included interventions to prevent HCV/HIV transmission in persons who inject drugs (PWID), including opioids. One SR evaluated interventions to prevent recurrent infectious endocarditis. There was sufficient and tentative SR of SRs-level evidence for the effectiveness of opioid substitution therapy (OST) in preventing HIV and HCV, respectively. We found tentative evidence to support effectiveness of needle/syringe exchange programs (NSP) in HIV prevention, and sufficient evidence to support effectiveness of the combined OST and NSP in HCV prevention. There was insufficient SR-level evidence to support or discount effectiveness of other interventions to prevent OUAIs. No SR focused on non-PWID populations. Conclusion: SR-level evidence supports the use of OST, NSP, and combined interventions for the reduction of HCV and HIV transmission in PWID. More research on prevention of other OUAIs and on prevention of OUAIs in non-PWID populations is urgently needed. Systematic Review Registration: Registered in PROSPERO on July 30, 2020. https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=195929, identifier: #195929.
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 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 | 0.025 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.017 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".