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Record W2901163685 · doi:10.1002/jia2.25197

Previous incarceration impacts access to hepatitis C virus (HCV) treatment among HIV‐HCV co‐infected patients in Canada

2018· article· en· W2901163685 on OpenAlexafffundabout
Nadine Kronfli, Roy Nitulescu, Joseph Cox, Erica E. M. Moodie, Alexander Wong, Curtis Cooper, M. John Gill, Sharon Walmsley, Valérie Martel‐Laferrière, Mark Hull, Marina B. Klein

Bibliographic record

VenueJournal of the International AIDS Society · 2018
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsAIDS VancouverUniversity of British ColumbiaHIV Legal NetworkUniversity Health NetworkUniversity of OttawaCentre Hospitalier de l’Université de MontréalUniversity of CalgaryUniversity of ReginaMcGill UniversityUniversity of SaskatchewanMcGill University Health Centre
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchStyrelsen för Internationellt Utvecklingssamarbete
KeywordsMedicineHepatitis CPrisonCohortHepatitis C virusPopulationHuman immunodeficiency virus (HIV)Internal medicineCohort studyDemographyImmunologyVirusEnvironmental health

Abstract

fetched live from OpenAlex

INTRODUCTION: The prevalence of hepatitis C virus (HCV) is far higher in prison settings than in the general population; thus, micro-elimination strategies must target people in prison to eliminate HCV. We aimed to examine incarceration patterns and determine whether incarceration impacts HCV treatment uptake among Canadian HIV-HCV co-infected individuals in the direct-acting antiviral (DAA) era. METHODS: The Canadian Co-Infection Cohort prospectively follows HIV-HCV co-infected people from 18 centres. HCV RNA-positive participants with available baseline information on incarceration history were included and followed from 21 November 2013 (when second-generation DAAs were approved by Health Canada) until 30 June 2017. A Cox proportional hazards model was used to assess the effect of time-updated incarceration status on time to treatment uptake, adjusting for patient-level characteristics known to be associated with treatment uptake in the DAA era. RESULTS: Overall, 1433 participants (1032/72% men) were included; 67% had a history of incarceration and 39% were re-incarcerated at least once. Compared to those never incarcerated, previously incarcerated participants were more likely to be Indigenous, earn <$1500 CAD/month, report current or past injection drug use and have poorly controlled HIV. There were 339 second-generation DAA treatment initiations during follow-up (18/100 person-years). Overall, 48% of participants never incarcerated were treated (27/100 person-years) compared to only 31% of previously incarcerated participants (15/100 person-years). Sustained virologic response (SVR) rates at 12 weeks were 95% and 92% respectively. After adjusting for other factors, participants with a history of incarceration (adjusted hazard ratio (aHR): 0.7, 95% CI: 0.5 to 0.9) were less likely to initiate treatment, as were those with a monthly income <$1500 (aHR: 0.7, 95% CI: 0.5 to 0.9) or who reported current injection drug use (aHR: 0.7, 95% CI: 0.4 to 1.0). Participants with undetectable HIV RNA (aHR: 2.1, 95% CI: 1.6 to 2.9) or significant fibrosis (aHR: 1.5, 95% CI: 1.2 to 1.9) were more likely to initiate treatment. CONCLUSIONS: The majority of HIV-HCV co-infected persons had a history of incarceration. Those previously incarcerated were 30% less likely to access treatment in the DAA era even after accounting for several patient-level characteristics. With SVR rates above 90%, HCV elimination may be possible if treatment is expanded for this vulnerable and neglected group.

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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.320

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.023
GPT teacher head0.334
Teacher spread0.310 · 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".

Quick stats

Citations26
Published2018
Admission routes3
Has abstractyes

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