Assessing the cost‐effectiveness of HAART for adults with HIV in England
Bibliographic record
Abstract
1 Royal Free Centre for HIV Medicine, Department of Primary Care and Population Sciences, Royal Free and University College Medical School, London, UK, 2 NPMS‐HHC, St. Stephen's Centre, Chelsea and Westminster Hospital, London, UK, 3 Global Health Outcomes, Glaxo Wellcome R and D, Greenford, Middlesex, UK, 4 Royal Free Centre for HIV Medicine, Department of Thoracic Medicine, Royal Free Hospital, London, UK and Joint Departments of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada Objective To assess the cost‐effectiveness of highly active antiretroviral therapy (HAART) compared with two nucleoside reverse transcriptase inhibitors (NRTIs) for HIV infected individuals. Design Different data sources on the clinical effects and costs of treatments were combined using a Markov model. Setting English HIV treatment centres. Perspective UK public finance. Interventions HAART – dual NRTI therapy plus a protease inhibitor or a non‐nucleoside reverse transcriptase inhibitor – vs. dual NRTI therapy. Participants Hypothetical cohorts of 1000 individuals infected with HIV. Outcome measures Projected life expectancy, cost‐effectiveness in UK£ per life‐year saved and per quality‐adjusted life‐years (QALYs) saved. Results Assuming a 2‐year additional treatment effect of therapy with HAART produced incremental cost‐effectiveness ratios of £14 602 per life‐year saved and £17 698 per QALY saved. Conclusions The results were sensitive to a number of assumptions including the cost of HAART and the discount rate, but they suggest that the use of HAART in England is at least moderately cost‐effective compared with treatment with two NRTIs alone.
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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 | 0.006 | 0.036 |
| 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".