Hepatitis C elimination among people who inject drugs: Challenges and recommendations for action within a health systems framework
Bibliographic record
Abstract
The burden of hepatitis C infection is considerable among people who inject drugs (PWID), with an estimated prevalence of 39%, representing an estimated 6.1 million people who have recently injected drugs living with hepatitis C infection. As such, PWID are a priority population for enhancing prevention, testing, linkage to care, treatment and follow-up care in order to meet World Health Organization (WHO) hepatitis C elimination goals by 2030. There are many barriers to enhancing hepatitis C prevention and care among PWID including poor global coverage of harm reduction services, restrictive drug policies and criminalization of drug use, poor access to health services, low hepatitis C 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. On 5 September 2017, the International Network of Hepatitis in Substance Users (INHSU) held a roundtable panel of international experts to discuss remaining challenges and future priorities for action from a health systems perspective. The WHO health systems framework comprises six core components: service delivery, health workforce, health information systems, medical procurement, health systems financing, and leadership and governance. Communication has been proposed as a seventh key element which promotes the central role of affected community engagement. This review paper presents recommended strategies for eliminating hepatitis C as a major public health threat among PWID and outlines future priorities for action within a health systems framework.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".