Evaluating Cost-Effectiveness of Antiretroviral Therapy over Time: A Cohort and Cost-Effectiveness Study
Bibliographic record
Abstract
OBJECTIVE: To estimate the costs and cost-effectiveness of introducing highly active antiretroviral therapy (HAART) in Denmark based on real-world evidence for the three treatment eras pre-HAART (1985-1995), early HAART (1996-2005), and late HAART (2006-2017). METHODS: We performed a cohort study using Danish clinical and administrative registries to estimate costs, quality-adjusted life-years (QALYs), and life-years (LY) gained per person living with human immunodeficiency virus (PLHIV) in three treatment eras. The study utilized Markov modeling for a health economic evaluation, which summarized inputs from real-world evidence and estimated the cost-effectiveness in 2017 prices of the introduction of HAART in Denmark. We performed deterministic and probabilistic sensitivity analyses to assess the robustness of the results. RESULTS: The total annual costs per PLHIV increased with the introduction of HAART for the index year but decreased in the incremental years and the last year of life. The total lifetime discounted (and undiscounted) cost for an average PLHIV was €91,010 (€128,981) in pre-HAART, €103,130 (€199,062) in early HAART, and €126,317 (€254,964) in late HAART. The estimated incremental cost-effectiveness ratios showed that early HAART was cost-effective compared with pre-HAART with an incremental cost-effectiveness ratio (ICER) of €1378 per QALY, and that late HAART was cost-effective compared with early HAART with an ICER of €7385 per QALY. Sensitivity analyses confirmed cost-effectiveness in all scenarios. CONCLUSIONS: The introduction and implementation of HAART in Danish healthcare was cost-effective, and in some scenarios, even disruptive, i.e., led to both cheaper and more effective care of PLHIV.
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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 | 0.025 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".