Eliminating Structural Barriers: The Impact of Unrestricted Access on Hepatitis C Treatment Uptake Among People Living With Human Immunodeficiency Virus
Bibliographic record
Abstract
BACKGROUND: High costs of direct-acting antivirals (DAAs) have led health-care insurers to limit access worldwide. Using a natural experiment, we evaluated the impact of removing fibrosis stage restrictions on hepatitis C (HCV) treatment initiation rates among people living with human immunodeficiency virus (HIV), and then examined who was left to be treated. METHODS: Using data from the Canadian HIV-HCV Coinfection Cohort, we applied a difference-in-differences approach. Changes in treatment initiation rates following the removal of fibrosis stage restrictions were assessed using a negative binomial regression with generalized estimating equations. The policy change was then specifically assessed among people who inject drugs (PWID). We then identified the characteristics of participants who remained to be treated using a modified Poisson regression. RESULTS: Between 2010-2018, there were a total of 585 HCV initiations among 1130 eligible participants. After removing fibrosis stage restrictions, DAA initiations increased by 1.8-fold (95% confidence interval [CI] 1.3-2.4) controlling for time-invariant differences and secular trends. Among PWID the impact appeared even stronger, with an adjusted incidence rate ratio of 3.6 (95% CI 1.8-7.4). However, this increased treatment uptake was not sustained. At 1 year following universal access, treatment rates declined to 0.8 (95% CI .5-1.1). Marginalized participants (PWID and those of indigenous ethnicity) and those disengaged from care were more likely to remain HCV RNA positive. CONCLUSIONS: After the removal of fibrosis restrictions, HCV treatment initiations nearly doubled immediately, but this treatment rate was not sustained. To meet the World Health Organization elimination targets, the minimization of structural barriers and adoption of tailored interventions are needed to engage and treat all vulnerable populations.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".