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Record W4412893321 · doi:10.1016/j.dadr.2025.100368

Impact of hepatitis C serostatus on health service utilization for opioid-related harms among individuals prescribed opioid agonist therapy: A longitudinal prospective cohort study

2025· article· en· W4412893321 on OpenAlex
Paige Guyatt, Glenda Babe, Anastasia Gayowsky, Tea Rosic, Myanca Rodrigues, Paxton Bach, Claire de Oliveira, Jeffrey H. Samet, Geneviève Kerkerian, Jessica Hann, Joanna C. Dionne, Aijaz Ahmed, Donghee Kim, Seonaid Nolan, Lehana Thabane, Zainab Samaan, Brittany B. Dennis

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueDrug and Alcohol Dependence Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of TorontoSt. Joseph’s Healthcare HamiltonUniversity of British ColumbiaUniversity of OttawaCentre for Addiction and Mental HealthBritish Columbia Centre on Substance UseMcMaster University
FundersNational Institute on Drug AbuseCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term CareMichael Smith Health Research BCInstitute for Clinical Evaluative Sciences
KeywordsSerostatusMedicineOpioidHepatitis COpioid epidemicProspective cohort studyAgonistLongitudinal studyCohortCohort studyInternal medicineFamily medicineHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

Varied substance use outcomes have been reported among individuals with a hepatitis C viral (HCV) infection on opioid agonist treatment (OAT) for opioid use disorder. Accordingly, the current study sought to evaluate the association between HCV serostatus, among other factors, and opioid-related acute health service utilization (e.g., emergency department [ED] visits and hospitalizations) among individuals prescribed OAT. Multi-site prospective cohort study data were used to characterize demographic characteristics, substance use patterns, and physical health amongst individuals prescribed OAT. Logistic regression models were built to estimate the association between HCV-seropositivity and opioid-related ED visits and hospitalizations over a three-year follow up period. Among 3430 participants, 10.6 % ( n = 365) were HCV-seropositive. In the follow-up period, 21.3 % ( n = 730) attended the ED and 8.7 % ( n = 298) were hospitalized for opioid related-harms. HCV-seropositivity was associated with an increased incidence of ED visits for opioid poisoning (9.0 % vs 4.9 % for participants who were HCV-seronegative, p < 0.01) and other opioid-related harms (22.5 % vs. 20.8 % for seronegative participants, p = 0.03). However, multiple logistical regression models showed no association between HCV serostatus and opioid-related health service utilization; rather, injection drug use was a significant predictor of opioid-related ED visits (OR 3.39, p < 0.01) and hospitalizations (OR 1.21, p = 0.01). Among individuals prescribed OAT, those with seropositive HCV have increased incidence of ED visits and hospitalizations for opioid-related harms, an association which may be driven by injection use practices. These findings highlight the importance of screening for injection use practices and health symptoms, as well as the potential role for targeting resources (e.g., harm reduction supplies, education regarding transmission) within this vulnerable subgroup. • Among individuals on opioid agonist therapy, those with hepatitis-C have more opioid-related hospital presentations. • However, hepatitis-C is not a prognostic factor for acute opioid-related presentations. • Injection drug use is the strongest predictor of acute opioid-related presentations.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.353
Teacher spread0.325 · 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