The Cost-Effectiveness of Triple Nucleoside Analogue Therapy Antiretroviral Regimens in the Treatment of HIV in the United Kingdom
Bibliographic record
Abstract
PURPOSE: Treatment of HIV infection has improved markedly in recent years due to the use of combination antiretroviral therapies. However, the cost of these treatments remains a concern for those who fund health services. This article reports the results of an economic evaluation to determine the incremental cost-effectiveness of triple combination nucleoside analogue (NA) therapy compared with dual NA therapy. METHOD: A Markov model was developed to assess the incremental cost effectiveness of triple therapy treatment versus dual therapy treatment. Clinical data was derived from published clinical trials and large observational cohorts whilst cost data was derived from a prospective database that monitors health care resource use in the UK HIV population. RESULTS: The model predicted that triple NA combination treatment extended life expectancy by an additional 1.2 years compared with dual therapy NA treatment with an incremental cost-effectiveness ratio of 8,419 pounds per life-year saved. The incremental cost per quality-adjusted life-year (QALY) was estimated to be between 10,072-16,168 pounds per QALY gained depending on the assumptions. CONCLUSION: The results show triple nucleoside analogue therapy to be a cost effective means of delaying HIV progression and extending life expectancy. However, further research into this issue is warranted.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".