A Monte Carlo Simulation of Advanced HIV Disease

Abstract

BACKGROUND: Disagreement exists among decision makers regarding the allocation of limited HIV patient care resources and, specifically, the comparative value of preventing opportunistic infections in late-stage disease. METHODS: A Monte Carlo simulation framework was used to evaluate a state-transition model of the natural history of HIV illness in patients with CD4 counts below 300/mm3 and to project the costs and consequences of alternative strategies for preventing AIDS-related complications. The authors describe the model and demonstrate how it may be employed to assess the cost-effectiveness of oral ganciclovir for prevention of cytomegalovirus (CMV) infection. RESULTS: Ganciclovir prophylaxis confers an estimated additional 0.7 quality-adjusted month of life at a net cost of $10,700, implying an incremental cost-effectiveness ratio of roughly $173,000 per quality-adjusted life year gained. Sensitivity analysis reveals that this baseline result is stable over a wide range of input data estimates, including quality of life and drug efficacy, but it is sensitive to CMV incidence and drug price assumptions. CONCLUSIONS: The Monte Carlo simulation framework offers decision makers a powerful and flexible tool for evaluating choices in the realm of chronic disease patient care. The authors have used it to assess HIV-related treatment options and continue to refine it to reflect advances in defining the pathogenesis and treatment of AIDS. Compared with alternative interventions, CMV prophylaxis does not appear to be a cost-effective use of scarce HIV clinical care funds. However, targeted prevention in patients identified to be at higher risk for CMV-related disease may warrant consideration.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 51 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.