Health Care Costs Associated with Hepatitis C: A Longitudinal Cohort Study
Bibliographic record
Abstract
BACKGROUND: Disease-specific estimates of medical costs are important for health policy decision making. OBJECTIVE: To identify predictors of health care costs associated with hepatitis C virus (HCV) seropositivity across disease phases. METHODS: HCV laboratory tests from the BC Centre for Disease Control were linked to administrative data pertaining to health services and drugs dispensed to estimate costs among case subjects and controls. The case group comprised HCV seropositive individuals (n=20,001), and the control group comprised single-tested, HCV seronegative persons (n=70,752) identified between January 1997 and December 2004. Subject observation time was assigned to the three following disease phases: initial phase (after diagnosis), late phase (late-stage liver disease) and predeath phase (12 months before death). Case subjects and controls were matched for age, sex and a propensity score within each phase to determine the net cost attributable to HCV seropositivity, and were adjusted for demographic and clinical factors. RESULTS: Costs increased with disease progression, with hospitalization being the highest cost component in all phases. Initial and late phase net costs (2005 Canadian dollars) were $1,850 and $6,000 per patient per year, respectively. Costs among case subjects were driven by age, comorbidities, mental illness, illicit drug use and HIV coinfection. While predeath case subject and control costs were virtually the same, costs were high and case subjects died at a younger age. CONCLUSION: HCV seropositivity is associated with increased medical costs driven by viral sequelae and medicosocial vulnerabilities (ie, mental illness, illicit drug use and HIV coinfection). Cost mitigation and health outcome improvements will require broad-based prevention programming to reduce vulnerabilities and HCV treatment to prevent disease progression, respectively.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".