The impact of new direct-acting antiviral therapy on the prevalence and undiagnosed proportion of chronic hepatitis C infection in Alberta: A model-based analysis
Bibliographic record
Abstract
Background: Understanding the impact of wider access to treatment on chronic hepatitis C (CHC) prevalence and the undiagnosed CHC proportion is important to achieving the World Health Organization's 2030 elimination targets. This research aimed to: (1) estimate the CHC prevalence and undiagnosed rates in Alberta, Canada; and (2) explore the impact of new direct-acting antiviral therapy on these rates since its introduction in 2014. Methods: This study adopted a two-step approach to estimate CHC prevalence and undiagnosed rates. This involved a population-based retrospective analysis of health administrative data for Alberta from 2002 to 2018 to generate CHC-related events for three birth cohorts: individuals born before 1945, individuals born between 1945 and 1965, and individuals born after 1965. A back-calculation method was employed to obtain historical prevalence and incidence estimates. Results: After the introduction of direct-acting antiviral treatment in 2014, the mean prevalence of CHC over all the birth cohorts fell by approximately 6.5% from 1.23% (95% CI: 0.97%-1.5%) to 1.15% (95% CI: 0.91%-1.45%) between 2015 and 2018. Similar trends were estimated for the 1945-1965 and the >1965 birth cohorts over the same period. Likewise, the mean proportion of undiagnosed CHC infections over all the birth cohorts fell by approximately 8.25% from 39.36% (95% CI: 30.08%-48.48%) to 36.36% (95% CI: 27.49%-45.31%) over the same period. A similar trend was experienced in all three birth cohorts. Conclusions: This is the first study to estimate CHC prevalence and undiagnosed proportions in Alberta using provincial health administrative data. These results could provide vital evidence to guide decisions about current and future hepatitis C virus strategies and help achieve the World Health Organization goal of eliminating hepatitis C in Canada by 2030.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".