Chronic hepatitis C infection is associated with higher incidence of extrahepatic cancers in a Canadian population based cohort
Bibliographic record
Abstract
Introduction: Chronic infection with hepatitis C virus (HCV) is an established risk factor for liver cancer. Although several epidemiologic studies have evaluated the risk of extrahepatic malignancies among people living with HCV, due to various study limitations, results have been heterogeneous. Methods: We used data from the British Columbia Hepatitis Testers Cohort (BC-HTC), which includes all individuals tested for HCV in the Province since 1990. We assessed hepatic and extrahepatic cancer incidence using data from BC Cancer Registry. Standardized incidence ratios (SIR) comparing to the general population of BC were calculated for each cancer site from 1990 to 2016. Results: In total, 56,823 and 1,207,357 individuals tested positive and negative for HCV, respectively. Median age at cancer diagnosis among people with and without HCV infection was 59 (interquartile range (IQR): 53-65) and 63 years (IQR: 54-74), respectively. As compared to people living without HCV, a greater proportion of people living with HCV-infection were men (66.7% vs. 44.7%, P-value <0.0001), had comorbidities (25.0% vs. 16.3%, P-value <0.0001) and were socially deprived (35.9% vs. 25.0%, P-value <0.0001). The SIRs for liver (SIR 33.09; 95% CI 29.80-36.39), anal (SIR: 2.57; 95% CI 1.52-3.63), oesophagus (SIR: 2.00; 95% CI 1.17-2.82), larynx (SIR: 3.24; 95% CI 1.21-5.27), lung (SIR: 2.20; 95% CI 1.82-2.58), and oral (SIR: 1.78; 95% CI 1.33-2.23) cancers were significantly higher among individuals living with HCV. The SIRs for bile duct and pancreatic cancers were significantly elevated among both individuals living with (SIR; 95% CI: 2.20; 1.27-3.14; 2.18; 1.57-2.79, respectively) and without HCV (SIR; 95% CI: 2.12; 1.88-2.36; 1.20; 1.11-1.28, respectively). Discussion/Conclusion: In this study, HCV infection was associated with increased incidence of several extrahepatic cancers. The elevated incidence of multiple cancers among negative HCV testers highlights the potential contributions of screening bias and increased cancer risks associated with factors driving acquisition of infection among this population compared to the general population. Early HCV diagnosis and treatment as well as public health prevention strategies are needed to reduce the risk of extrahepatic cancers among people living with HCV and potentially populations who are at higher risk of HCV infection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".