The Cost-effectiveness of Screening for Chronic Hepatitis B Infection in the United States
Bibliographic record
Abstract
BACKGROUND: Hepatitis B virus (HBV) continues to cause significant morbidity and mortality in the United States. Current guidelines suggest screening populations with a prevalence of ≥2%. Our objective was to determine whether this screening threshold is cost-effective and whether screening lower-prevalence populations might also be cost-effective. METHODS: We developed a Markov state transition model to examine screening of asymptomatic outpatients in the United States. The base case was a 35-year-old man living in a region with an HBV infection prevalence of 2%. Interventions (versus no screening) included screening for Hepatitis B surface antigen followed by treatment of appropriate patients with (1) pegylated interferon-α2a for 48 weeks, (2) a low-cost nucleoside or nucleotide agent with a high rate of developing viral resistance for 48 weeks, (3) prolonged treatment with low-cost, high-resistance nucleoside or nucleotide, or (4) prolonged treatment with a high-cost nucleoside or nucleotide with a low rate of developing viral resistance. Effectiveness was measured in quality-adjusted life years (QALYs) and costs in 2008 US dollars. RESULTS: Screening followed by treatment with a low-cost, high-resistance nucleoside or nucleotide was cost-effective ($29,230 per QALY). Sensitivity analyses revealed that screening costs <$50,000 per QALY in extremely low-risk populations unless the prevalence of chronic HBV infection is <.3%. CONCLUSIONS: The 2% threshold for prevalence of chronic HBV infection in current Centers for Disease Control and Prevention/US Public Health Service screening guidelines is cost-effective. Furthermore, screening of adults in the United States in lower-prevalence populations (eg, as low as .3%) also is likely to be cost-effective, suggesting that current health policy should be reconsidered.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".