Cost Effectiveness of Screening Strategies for Early Identification of HIV and HCV Infection in Injection Drug Users
Bibliographic record
Abstract
OBJECTIVE: To estimate the cost, effectiveness, and cost effectiveness of HIV and HCV screening of injection drug users (IDUs) in opioid replacement therapy (ORT). DESIGN: Dynamic compartmental model of HIV and HCV in a population of IDUs and non-IDUs for a representative U.S. urban center with 2.5 million adults (age 15-59). METHODS: We considered strategies of screening individuals in ORT for HIV, HCV, or both infections by antibody or antibody and viral RNA testing. We evaluated one-time and repeat screening at intervals from annually to once every 3 months. We calculated the number of HIV and HCV infections, quality-adjusted life years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs). RESULTS: Adding HIV and HCV viral RNA testing to antibody testing averts 14.8-30.3 HIV and 3.7-7.7 HCV infections in a screened population of 26,100 IDUs entering ORT over 20 years, depending on screening frequency. Screening for HIV antibodies every 6 months costs $30,700/QALY gained. Screening for HIV antibodies and viral RNA every 6 months has an ICER of $65,900/QALY gained. Strategies including HCV testing have ICERs exceeding $100,000/QALY gained unless awareness of HCV-infection status results in a substantial reduction in needle-sharing behavior. DISCUSSION: Although annual screening for antibodies to HIV and HCV is modestly cost effective compared to no screening, more frequent screening for HIV provides additional benefit at less cost. Screening individuals in ORT every 3-6 months for HIV infection using both antibody and viral RNA technologies and initiating ART for acute HIV infection appears cost effective.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".