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Record W4280636907 · doi:10.1093/ofid/ofac145

Importance of Opioid Agonist Therapy to Reduce Injection-Related Infections

2022· article· en· W4280636907 on OpenAlexaff
Victoria Weaver, Mary Clare Kennedy

Bibliographic record

VenueOpen Forum Infectious Diseases · 2022
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsBritish Columbia Centre on Substance UseUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNational Institute on Drug Abuse
KeywordsMedicineAgonistOpioidOpioid epidemicIntensive care medicinePharmacologyInternal medicineReceptor

Abstract

fetched live from OpenAlex

To the Editor—We read with interest the recent study by Harvey and colleagues describing the effectiveness of the “Six Moments of Infection Prevention in Injection Drug Use” educational toolkit for clinicians to prevent infection in people who inject drugs (PWID) [1]. The toolkit focuses on the importance of new, sterile needles and other injection equipment, as well as cleaning the skin prior to each injection. Training using this toolkit was delivered to 75 providers—56% of whom were medical practitioners or trainees, with administration of pre- and posttraining surveys to assess knowledge, attitudes, and comfort with the harm reduction interventions described. They note that following this training, 86.6% of respondents reported intention to incorporate this model into their own practices. The use of a global approach to infection prevention, modeled on the World Health Organization’s “Five Moments for Hand Hygiene” campaign [2], provides an important framework for infectious disease clinicians to understand widely used harm reduction strategies and identify ways to reduce the risk of injection-related infections. Along with the 6 moments of infection prevention that Harvey and colleagues describe, we would like to suggest that readers also consider initiation of opioid agonist therapy (OAT) as an additional important tool to prevent infections among PWID. OAT—which includes methadone and buprenorphine (either on its own or co-formulated with naloxone) is an evidence-based intervention intended to reduce illicit opioid use, cravings, and death among people with opioid use disorder. Receipt of OAT has been shown to reduce the risk of human immunodeficiency virus (HIV) and hepatitis C virus acquisition [3, 4] and promote HIV viral suppression [5]. Additionally, in-hospital initiation of OAT has been found to reduce the risk of recurrent injection-related skin and soft tissue infections [6] as well as 1-year all-cause rehospitalization following admission for infective endocarditis among people with opioid use disorder [7]. In light of the ongoing overdose crisis in North America, it is also notable that initiation of OAT among people with opioid use disorder hospitalized for endocarditis has been found to reduce risk of subsequent opioid-related overdose [7]. There has been an increased call for the integration of infectious diseases (ID) and addiction medicine services [8], and ID care providers have a unique opportunity to assess for and treat opioid use disorder in patients experiencing injection-related infections. However, in a recent survey of ID providers in the United States, only 18 of 526 respondents who reported treating PWID as part of their clinical practice reported being able to prescribe buprenorphine [9]. As such, we believe that enhanced training and resources to support the expansion of OAT prescribing among ID care providers represents a promising strategy to reduce morbidity and mortality among PWID. Indeed, a recent modeling study estimated that expanded prescribing of OAT in hospital settings alone would reduce hospitalizations by 3.2% among people who inject opioids in the United States [10]. As source control is one of the most important aspects of ID practice, we encourage readers to consider the importance of OAT, in addition to the toolkit provided by Harvey and colleagues, in further reducing injection-related infections and other serious harms among PWID. Author contributions. Report conception: V. K. W. Drafting of the manuscript: V. K. W., M. C. K. Revision of manuscript: V. K. W., M. C. K. Both authors approve the submitted manuscript version and have agreed to be personally accountable for any questions related to accuracy or integrity of any part of the work. Acknowledgments. The authors would like to thank the staff of the International Collaborative Addiction Medicine Research Fellowship at the British Columbia Centre on Substance Use for their administrative and research support. Financial support. This work was supported by the National Institute on Drug Abuse (grant number R25-DA037756 to V. K. W.). Potential conflicts of interest. Both authors: No reported conflicts of interest. Both authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.005
Open science0.0030.001
Research integrity0.0120.022
Insufficient payload (model declined to judge)0.0070.005

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.

Opus teacher head0.013
GPT teacher head0.301
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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