Assessing Hepatitis C Burden and Treatment Effectiveness through the British Columbia Hepatitis Testers Cohort (BC-HTC): Design and Characteristics of Linked and Unlinked Participants
Bibliographic record
Abstract
BACKGROUND: The British Columbia (BC) Hepatitis Testers Cohort (BC-HTC) was established to assess and monitor hepatitis C (HCV) epidemiology, cost of illness and treatment effectiveness in BC, Canada. In this paper, we describe the cohort construction, data linkage process, linkage yields, and comparison of the characteristics of linked and unlinked individuals. METHODS: The BC-HTC includes all individuals tested for HCV and/or HIV or reported as a case of HCV, hepatitis B (HBV), HIV or active tuberculosis (TB) in BC linked with the provincial health insurance client roster, medical visits, hospitalizations, drug prescriptions, the cancer registry and mortality data using unique personal health numbers. The cohort includes data since inception (1990/1992) of each database until 2012/2013 with plans for annual updates. We computed linkage rates by year and compared the characteristics of linked and unlinked individuals. RESULTS: Of 2,656,323 unique individuals available in the laboratory and surveillance data, 1,427,917(54%) were included in the final linked cohort, including about 1.15 million tested for HCV and about 1.02 million tested for HIV. The linkage rate was 86% for HCV tests, 89% for HCV cases, 95% for active TB cases, 48% for HIV tests and 36% for HIV cases. Linkage rates increased from 40% for HCV negatives and 70% for HCV positives in 1992 to ~90% after 2005. Linkage rates were lower for males, younger age at testing, and those with unknown residence location. Linkage rates for HCV testers co-infected with HIV, HBV or TB were very high (90-100%). CONCLUSION: Linkage rates increased over time related to improvements in completeness of identifiers in laboratory, surveillance, and registry databases. Linkage rates were higher for HCV than HIV testers, those testing positive, older individuals, and females. Data from the cohort provide essential information to support the development of prevention, care and treatment initiatives for those infected with HCV.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".