Impact of the COVID-19 pandemic on hepatitis C virus screening in provincial prisons in Montreal, Quebec, Canada
Bibliographic record
Abstract
Background: Little is known about the impact of the COVID-19 pandemic on hepatitis C (HCV) screening efforts in carceral settings. We explored the impact of the pandemic on HCV screening in two of Quebec's largest provincial prisons. Methods: Retrospective data of HCV-related laboratory tests between July 2018 and February 2022 at l'Établissement de détention de Montréal (EDM) and l'Établissement de détention de Rivière-des-Prairies (EDRDP) were obtained. To examine the association between the pandemic and the number of HCV-antibody (HCV-Ab) tests, a three-level time period variable was created: pre-outbreak, outbreak, and post-outbreak. Negative binomial regression (with monthly admissions as an offset) was used to assess the change in HCV-Ab tests across time periods and by prisons. Adjusted odds ratios (aOR) with 95% confidence intervals (95% CI) were calculated. Results: A total of 1,790 HCV-Ab tests were performed; 56 (3%) were positive. Among these, 44 (79%) HCV RNA tests were performed; 23 (52%) were positive. There was a significant decrease in HCV-Ab screening at EDM during the outbreak (aOR 0.29; 95% CI 0.17-0.48) and post-outbreak (aOR 0.49; 95% CI 0.35-0.69) periods, compared to the pre-outbreak period. There was no significant change in HCV-Ab screening at EDRDP during the outbreak (aOR 0.98; 95% CI 0.49-2.11) but a significant increase in HCV-Ab screening post-outbreak (aOR 1.66; 95% CI 1.04-2.72). Conclusion: The COVID-19 pandemic negatively affected HCV screening at EDM but had minimal impact at EDRDP. To eliminate HCV from carceral settings, minimizing screening interruptions during future outbreaks and combined HCV/SARS-CoV-2 screening 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 | 0.005 |
| 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 | 0.001 | 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".