The Hepatitis C treatment revolution: Are key HIV-Hepatitis C Co-infected populations being left behind?
Bibliographic record
Abstract
The emergence of direct-acting antivirals (DAAs), for the treatment of hepatitis C virus (HCV), marked one of the most significant advances in modern therapeutics. Unlike previous generations of therapies, DAAs are well-tolerated and cure >90% of chronically infected individuals. As such, in 2016, the World Health Organization defined targets to scale up screening, access to treatment and harm reduction to eliminate HCV as a public health threat by 2030. Nonetheless increasing HCV treatment initiation rates, particularly among marginalized populations, remains a significant public health challenge. This thesis addressed fundamental issues regarding the identification and quantification of barriers to DAA treatment initiation, in addition to assessing the real-world impact of treatment on individuals in Canada who are co-infected with HIV-HCV. To this end, I used data from the Canadian HIV-HCV Co-Infection Cohort (CCC), one of the largest prospective cohorts in the world. Manuscript #1 examined the generalizability of the clinical trials used to license DAAs. Here I found only a minority of CCC participants (6-43%) would have been eligible for enrolment into these trials. The majority of the exclusions appeared to be related to improving treatment outcomes by not including those at higher risk of poor adherence. This highlighted the need to evaluate the real-world impact of DAAs on access to treatment and health outcomes. Manuscript #2 evaluated DAA treatment uptake by key populations and their subsequent treatment response in a real-world setting. HCV treatment rates increased by more than three times after the introduction of DAAs (8 initiations to 28 per 100-person-years). But, using a multivariate Cox proportional hazards model, I found people who inject drugs (PWID) and more generally, people with lower income were less likely to initiate treatment. Reflective of reimbursement restrictions, people with significant liver fibrosis were more likely to initiate treatment. As the price of DAAs were reduced and reimbursement restrictions were broadened. Manuscript #3 evaluated the impact of removing fibrosis stage restrictions on HCV treatment initiation. I applied a difference-in-differences approach using a negative binomial regression with generalized estimating equations to assess the impact of the policy change. Removing fibrosis stage restrictions, increased treatment uptake by 1.8 times (95% CI, 1.4, 2.5) accounting for temporal trends and the time-invariant difference between provinces. Among PWID, the impact appeared even stronger; adjusted incidence rate ratio (aIRR), 3.6 (95% CI 1.8, 7.4). Four years after the advent of DAAs, marginalized participants (PWID and those of Indigenous ethnicity) and those disengaged from care, remained more likely to require treatment. Manuscript #4 investigated the real-world impact of successful DAA treatment on health-related quality of life (HR-QoL) using a segmented multivariate linear mixed model. In contrast to clinical trial results, we observed only modest improvements in HR-QoL following a sustained virologic response with DAA therapy. In addition to these substantive objectives, this dissertation also contributes to the advancement of epidemiological methods by including two published tutorials detailing the methods used to answer the research questions for the third and fourth manuscripts, the difference-in-differences approach and segmented mixed effect models, respectively. This is an unprecedented time in clinical medicine. DAAs have transformed clinical practise by curing a chronic infection in the vast majority of patients in less than 12 weeks, but challenges remain. This work describes and quantifies barriers to HCV treatment uptake that can inform HCV elimination efforts currently underway worldwide
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".