Mortality among British Columbians testing for hepatitis C antibody
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) infection is a major preventable and treatable cause of morbidity and mortality. The ability to link population based centralized laboratory HCV testing data with administrative databases provided a unique opportunity to compare mortality between HCV seronegative and seropositive individuals. Through the use of laboratory testing patterns and results, the objective of this study was to differentiate the viral effects of mortality due to HCV infection from risk behaviours/activities that are associated with acquisition of HCV infection. METHODS: Serological testing data from the British Columbia (BC) Centre for Disease Control Public Health Microbiology and Reference Laboratory from 1992-2004 were linked to the BC Vital Statistics Agency death registry. Four groups of HCV testers were defined by their HCV antibody (anti-HCV) testing patterns: single non-reactive (SNR); serial multiple tested non-reactive (MNR); reactive at initial testing (REAC); and seroconverter (SERO) (previously seronegative followed by reactive, a marker for incident infection). Standardized mortality ratios (SMRs) were calculated to compare the relative risk of all cause and disease specific mortality to that of the BC population for each serological group. Time dependent Cox proportional hazard regression was used to compare hazard ratios (HRs) among HCV serological groups. RESULTS: All anti-HCV testers had higher SMRs than the BC population. Referent to the SNR group, the REAC group had higher risks for liver (HR: 9.62; 95% CI=8.55-10.87) and drug related mortality (HR: 13.70; 95% CI=11.76-16.13). Compared to the REAC group, the SERO group had a lower risk for liver (HR: 0.53; 95% CI=0.24-0.99), but a higher risk for drug related mortality (HR: 1.54; 95% CI=1.12-2.05). CONCLUSIONS: These findings confirm that individuals who test anti-HCV positive have increased mortality related to progressive liver disease, and that a substantial proportion of the mortality is attributable to drug use and risk behaviours/activities associated with HCV acquisition. Mortality reduction in HCV infected individuals will require comprehensive prevention programming to reduce the harms due to behaviours/activities which relate to HCV acquisition, as well as HCV treatment to prevent progression of chronic liver disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".