The Impact of COVID-19 and Related Public Health Measures on Hepatitis C Testing in Ontario, Canada
Bibliographic record
Abstract
The COVID-19 pandemic disrupted progress towards global HCV elimination goals by interrupting essential health services in Canada and globally. We aimed to evaluate the effect of the pandemic on hepatitis C virus (HCV) testing rates in a population-based cohort study in Ontario using health administrative data. All residents with records of either HCV antibody or ribonucleic acid (RNA) tests were included. Monthly testing rate per 1000 population were compared during the pre-pandemic (01/01/2015-29/02/2020) and pandemic (01/03/2020-31/12/2022) periods using interrupted time series models, stratified by sex, homelessness, human immunodeficiency virus (HIV), and immigration status, and people who inject drugs (PWID). The HCV testing rate followed a statistically significant upward trend before the pandemic, dropping at its onset with 1.38/1000 fewer individuals initiating testing monthly. Compared to counterfactual estimates, the observed monthly number of people tested per 1000 population was lower by 1.41 (95% CI: 1.18-1.64) in 2020 (May-Dec), 1.17 (95% CI: 0.99-1.36) in 2021, and 1.41 (95% CI: 1.22-1.59) in 2022, corresponding to relative reductions of 47%, 34%, and 41%, respectively. Testing rates remained below expected levels across all subgroups throughout 2020-2022, with the greatest absolute declines observed among people co-infected with HIV, people experiencing homelessness, and PWID. Tailored, equity-focused interventions are needed to address these persistent gaps in HCV testing, without which Canada's progress toward its 2030 elimination targets remains at risk.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".