The effect of first-wave COVID-19 restrictions on HCV testing in Alberta, Canada: A trend analysis from 2019 to 2022
Bibliographic record
Abstract
Background: Prior to the COVID-19 pandemic, Alberta was on track to meet national HCV elimination targets by 2030. However, it is unclear how the pandemic has affected progress. Here, we aim to assess the impact of first-wave COVID-19 restrictions on Alberta HCV testing trends. Methods: HCV testing information was extracted from the provincial public health laboratory from 2019 to 2022. HCV antibody and RNA testing were categorized into (1) number ordered, (2) number positive, and (3) percent positivity, and stratified by HCV history status. Testing trends were evaluated across locations engaging high-risk individuals and priority demographics. An interrupted time-series analysis was used to identify average monthly testing rates before, during, and after first-wave COVID-19 restrictions. Results: Overall, HCV testing trends were significantly affected by COVID-19 restrictions in April 2020. Average monthly rates decreased by 98.39 antibody tests ordered per 100,000 among individuals without an HCV history and by 1.78 RNA tests ordered per 100,000 among those with an HCV history. While antibody and RNA testing trends started to rebound in the follow-up period relative to pre-restriction period, testing levels in the follow-up period remained below pre-restriction levels for all groups, except for addiction/recovery centres and emergency room/acute care facilities, which increased. Conclusions: If rates are to return to pre-restriction levels and elimination goals are to be met, more work is needed to engage individuals in HCV testing. As antibody testing rates are rebounding, reengaging those with a history of HCV for viral load monitoring and treatment should be prioritized.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".