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Record W2741725136 · doi:10.7448/ias.20.1.22146

Elimination of HCV as a public health concern among people who inject drugs by 2030 – What will it take to get there?

2017· article· en· W2741725136 on OpenAlexaff
Jason Grebely, Gregory J. Dore, Sébastien Morin, Jürgen K. Rockstroh, Marina B. Klein

Bibliographic record

VenueJournal of the International AIDS Society · 2017
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsMcGill University Health Centre
FundersNational Health and Medical Research CouncilDepartment of Health and Aged Care, Australian Government
KeywordsMedicineHarm reductionPublic healthPopulationDecriminalizationHepatitis CSofosbuvirEnvironmental healthIntensive care medicineRibavirinVirologyNursingHepatitis C virus

Abstract

fetched live from OpenAlex

INTRODUCTION: Globally, there is a considerable burden of HCV and HIV infections among people who inject drugs (PWID) and transmission of both infections continues. Needle and syringe programme (NSP) and opioid substitution therapy (OST) coverage remains low, despite evidence demonstrating their prevention benefit. Direct-acting antiviral therapies (DAA) with HCV cure >95% among PWID provide an opportunity to reverse rising trends in HCV-related morbidity and mortality and reduce incidence. However, HCV testing, linkage to care, and treatment remain low due to health system, provider, societal, and patient barriers. Between 2015 and 2030, WHO targets include reducing new HCV infections by 80% and HCV deaths by 65%, and increasing HCV diagnoses from <5% to 90% and number of eligible persons receiving HCV treatment from <1% to 80%. This commentary discusses why PWID should be considered as a priority population in these efforts, reasons why this goal could be attainable among PWID, challenges that need to be overcome, and key recommendations for action. DISCUSSION: Challenges to HCV elimination as a global health concern among PWID include poor global coverage of harm reduction services, restrictive drug policies and criminalization of drug use, poor access to health services, low HCV testing, linkage to care and treatment, restrictions for accessing DAA therapy, and the lack of national strategies and government investment to support WHO elimination goals. Key recommendations for action include reforming drug policies (decriminalization of drug use and/or possession, or providing alternatives to imprisonment for PWID; decriminalization of the use and provision of sterile needles-syringes; and legalization of OST for people who are opioid dependent), scaling up and improving funding for harm reduction services, making health services accessible for PWID, supporting community empowerment and community-based programmes, improving access to affordable diagnostics and medicines, and eliminating stigma, discrimination, and violence against PWID. CONCLUSIONS: The ambitious targets for HCV elimination set by WHO are achievable in many countries, but will require researchers, healthcare providers, policy makers, affected communities, advocates, the pharmaceutical and diagnostics industries, and governments around the world to work together to make this happen.

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)0.007
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.005
Open science0.0020.002
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0080.002

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 head0.049
GPT teacher head0.368
Teacher spread0.320 · 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 designTheoretical or conceptual
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

Citations170
Published2017
Admission routes1
Has abstractyes

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