A. David Paltiel

The Survival Benefits of AIDS Treatment in the United States

BACKGROUND As widespread adoption of potent combination antiretroviral therapy (ART) reaches its tenth year, our objective was to quantify the cumulative survival benefits of acquired immunodeficiency syndrome (AIDS) care in the United States. METHODS We defined eras corresponding to advances in standards of human immunodeficiency virus (HIV) disease care, including opportunistic infection prophylaxis, treatment with ART, and the prevention of mother-to-child transmission (pMTCT) of HIV. Per-person survival benefits for each era were determined using a mathematical simulation model. Published estimates provided the number of adult patients with new diagnoses of AIDS who were receiving care in the United States from 1989 to 2003. RESULTS Compared with survival associated with untreated HIV disease, per-person survival increased 0.26 years with Pneumocystis jiroveci pneumonia prophylaxis alone. Four eras of increasingly effective ART in addition to prophylaxis resulted in per-person survival increases of 7.81, 11.05, 11.57, and 13.33 years, compared with the absence of treatment. Treatment for patients with AIDS in care in the United States since 1989 yielded a total survival benefit of 2.8 million years. pMTCT averted nearly 2900 infant infections, equivalent to 137,000 additional years of survival benefit. CONCLUSIONS At least 3.0 million years of life have been saved in the United States as a direct result of care of patients with AIDS, highlighting the significant advances made in HIV disease treatment.

The Cost Effectiveness of Combination Antiretroviral Therapy for HIV Disease

BACKGROUND Combination antiretroviral therapy with a combination of three or more drugs has become the standard of care for patients with human immunodeficiency virus (HIV) infection in the United States. We estimated the clinical benefits and cost effectiveness of three-drug antiretroviral regimens. METHODS We developed a mathematical simulation model of HIV disease, using the CD4 cell count and HIV RNA level as predictors of the progression of disease. Outcome measures included life expectancy, life expectancy adjusted for the quality of life, lifetime direct medical costs, and cost effectiveness in dollars per quality-adjusted year of life gained. Clinical data were derived from major clinical trials, including the AIDS Clinical Trials Group 320 Study. Data on costs were based on the national AIDS Cost and Services Utilization Survey, with drug costs obtained from the Red Book. RESULTS For patients similar to those in the AIDS Clinical Trials Group 320 Study (mean CD4 cell count, 87 per cubic millimeter), life expectancy adjusted for the quality of life increased from 1.53 to 2.91 years, and per-person lifetime costs increased from

Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-6.

45,460 to

Model Parameter Estimation and Uncertainty: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6

77,300 with three-drug therapy as compared with no therapy. The incremental cost per quality-adjusted year of life gained, as compared with no therapy, was

HIV Preexposure Prophylaxis in the United States: Impact on Lifetime Infection Risk, Clinical Outcomes, and Cost-Effectiveness

23,000. On the basis of additional data from other major studies, the cost-effectiveness ratio for three-drug therapy ranged from

Expanded HIV Screening in the United States: Effect on Clinical Outcomes, HIV Transmission, and Costs

13,000 to

Conceptualizing a Model A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force–2

23,000 per quality-adjusted year of life gained. The initial CD4 cell count and drug costs were the most important determinants of costs, clinical benefits, and cost effectiveness. CONCLUSIONS Treatment of HIV infection with a combination of three antiretroviral drugs is a cost-effective use of resources.

Impact of Obesity and Knee Osteoarthritis on Morbidity and Mortality in Older Americans

A model’s purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in making better decisions. From a policy perspective, the value of a model-based analysis lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. Different concepts relating to uncertainty in decision modeling are explored. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty to consider. The article argues that the estimation of point estimates and uncertainty in parameters is part of a single process and explores the link between parameter uncertainty through to decision uncertainty and the relationship to value-of-information analysis. The article also makes extensive recommendations around the reporting of uncertainty, both in terms of deterministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique, alongside cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis.

Racial and Sex Disparities in Life Expectancy Losses among HIV-Infected Persons in the United States: Impact of Risk Behavior, Late Initiation, and Early Discontinuation of Antiretroviral Therapy

A models purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in making better decisions. From a policy perspective, the value of a model-based analysis lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. Different concepts relating to uncertainty in decision modeling are explored. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty to consider. The article argues that the estimation of point estimates and uncertainty in parameters is part of a single process and explores the link between parameter uncertainty through to decision uncertainty and the relationship to value of information analysis. The article also makes extensive recommendations around the reporting of uncertainty, in terms of both deterministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique, alongside cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis.

When to Start Antiretroviral Therapy in Resource-Limited Settings

BACKGROUND The combination of tenofovir and emtricitabine shows promise as HIV preexposure prophylaxis (PrEP). We sought to forecast clinical, epidemiologic, and economic outcomes of PrEP, taking into account uncertainties regarding efficacy, the risks of developing drug resistance and toxicity, behavioral disinhibition, and drug costs. METHODS We adapted a computer simulation of HIV acquisition, detection, and care to model PrEP among men who have sex with men and are at high risk of HIV infection (i.e., 1.6% mean annual incidence of HIV infection) in the United States. Base-case assumptions included 50% PrEP efficacy and monthly tenofovir-emtricitabine costs of