A population‐based study of reported hepatitis C diagnoses from 1998 to 2018 in immigrants and nonimmigrants in Quebec, Canada
Bibliographic record
Abstract
Immigrants living in low hepatitis C (HCV) prevalence countries bear a disproportionate HCV burden, but there are limited HCV population-based studies focussed on this population. We estimated rates and trends of reported HCV diagnoses over a 20-year period in Quebec, Canada, to investigate subgroups with the highest rates and changes over time. A population-based cohort of all reported HCV diagnoses in Quebec (1998-2018) linked to health administrative and immigration databases. HCV rates, rate ratios (RR) and trends overall and stratified by immigrant status and country of birth were estimated using Poisson regression. Among 38,348 HCV diagnoses, 14% occurred in immigrants, a median of 7.5 years after arrival. The average annual HCV rate/100,000 decreased for immigrants and nonimmigrants, but the risk (RR) among immigrants increased over the study period [35.7 vs. 34.5 (RR = 1.03) and 18.4 vs. 12.7 (1.45) between 1998-2008 and 2009-2018]. Immigrants from middle-income Europe & Central Asia [55.8 (RR = 4.39)], sub-Saharan Africa [51.7 (RR = 4.06)] and South Asia [32.8 (RR = 2.58)] had the highest rates between 2009 and 2018. Annual HCV rates decreased more slowly among immigrants vs. nonimmigrants (-5.9% vs. -8.9%, p < 0.001), resulting in a 2.5-fold (9%-21%) increase in the proportion of HCV diagnoses among immigrants (1998-2018). The slower decline in HCV rates among immigrants over the study period highlights the need for targeted screening for this population, particularly those from sub-Saharan Africa, Asia and middle-income Europe. These data can inform micro-elimination efforts in Canada and other low-HCV-prevalence countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".