The Hepatitis C treatment revolution: Are key HIV-Hepatitis C Co-infected populations being left behind?
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».