Hospitalizations in Immigrants and Nonimmigrants Diagnosed With Chronic Hepatitis C Infection in Québec
Bibliographic record
Abstract
BACKGROUND: Rates of hospitalization due to chronic hepatitis C virus (HCV) are increasing in Canada and the United States. A large proportion of immigrants originate from countries with intermediate to high HCV prevalence but are not screened for HCV post-arrival and may therefore have increased risks of liver-related complications and hospitalization. METHODS: We conducted a retrospective cohort study of reported HCV cases in Québec, Canada, from 1998 to 2007 that were linked to administrative health databases. Outcomes included all-cause and liver-related hospitalizations and in-hospital days in immigrants compared with nonimmigrants adjusted for age, sex, and comorbidities. RESULTS: We identified 20 139 HCV cases; 9% (N = 1821) were immigrants. At diagnosis, immigrants were older (47.6 vs 43.2 years) and more likely to have hepatocellular carcinoma (HCC; 0.93% vs 0.31%), while nonimmigrants were 2- to 10-fold more likely to have substance use-related comorbidities. Mean time to HCV diagnosis after arrival was 9.8 years. Nonimmigrants had higher rates of all-cause hospitalization (adjusted rate ratio [95% confidence interval], 1.42 [1.35-1.47]), driven by mental illness and injury and/or poisoning. Unadjusted liver-related hospitalization rates were similar between cohorts. After adjustment, immigrant status was associated with lower rates of liver-related hospitalization (0.68 [.53-.88]). CONCLUSIONS: Higher burden of all-cause hospitalization in nonimmigrants likely reflects more prevalent behavioral comorbidities. Similar liver-related hospitalization rates appear to be driven by older age in immigrants who were more likely to have HCC at diagnosis possibly reflecting delayed HCV diagnosis. These findings suggest that earlier screening and treatment in immigrants could play an important role in preventing HCV complications in this population.
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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 | 0.002 |
| 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 | 0.000 |
| 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".