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      Model-Based Methods to Translate Adolescent Medicine Trials Network for HIV/AIDS Interventions Findings Into Policy Recommendations: Rationale and Protocol for a Modeling Core (ATN 161)

      research-article
      , MD, MPH 1 , 2 , , , DSc, MPH 3 , , MD, ScM 4 , , MD, MPH 2 , 5 , , PhD 6 , , PhD 7 , , MD 8 , , PhD 9 , , PhD 10 , , PhD, MPH 11 , , MD, MPH 12 , , PhD 13 , , PhD 14 , , MD 15 , , PhD 11 , , DrPH 16 , , MD 17 , , MD, MSc 2 , 18 , 19 , , BA 2 , , PhD 20 , , MD, MPH 2 , 5
      , , , ,
      (Reviewer), (Reviewer), (Reviewer)
      JMIR Research Protocols
      JMIR Publications
      adolescent, costs and cost analysis, health policy, HIV, medication adherence, modeling, retention in care, youth

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          Abstract

          Background

          The United States Centers for Disease Control and Prevention estimates that approximately 60,000 US youth are living with HIV. US youth living with HIV (YLWH) have poorer outcomes compared with adults, including lower rates of diagnosis, engagement, retention, and virologic suppression. With Adolescent Medicine Trials Network for HIV/AIDS Interventions (ATN) support, new trials of youth-centered interventions to improve retention in care and medication adherence among YLWH are underway.

          Objective

          This study aimed to use a computer simulation model, the Cost-Effectiveness of Preventing AIDS Complications (CEPAC)-Adolescent Model, to evaluate selected ongoing and forthcoming ATN interventions to improve viral load suppression among YLWH and to define the benchmarks for uptake, effectiveness, durability of effect, and cost that will make these interventions clinically beneficial and cost-effective.

          Methods

          This protocol, ATN 161, establishes the ATN Modeling Core. The Modeling Core leverages extensive data—already collected by successfully completed National Institutes of Health–supported studies—to develop novel approaches for modeling critical components of HIV disease and care in YLWH. As new data emerge from ongoing ATN trials during the award period about the effectiveness of novel interventions, the CEPAC-Adolescent simulation model will serve as a flexible tool to project their long-term clinical impact and cost-effectiveness. The Modeling Core will derive model input parameters and create a model structure that reflects key aspects of HIV acquisition, progression, and treatment in YLWH. The ATN Modeling Core Steering Committee, with guidance from ATN leadership and scientific experts, will select and prioritize specific model-based analyses as well as provide feedback on derivation of model input parameters and model assumptions. Project-specific teams will help frame research questions for model-based analyses as well as provide feedback regarding project-specific inputs, results, sensitivity analyses, and policy conclusions.

          Results

          This project was funded as of September 2017.

          Conclusions

          The ATN Modeling Core will provide critical information to guide the scale-up of ATN interventions and the translation of ATN data into policy recommendations for YLWH in the United States.

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          Most cited references48

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

          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.
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            Expanded screening for HIV in the United States--an analysis of cost-effectiveness.

            Although the Centers for Disease Control and Prevention (CDC) recommend routine HIV counseling, testing, and referral (HIVCTR) in settings with at least a 1 percent prevalence of HIV, roughly 280,000 Americans are unaware of their human immunodeficiency virus (HIV) infection. The effect of expanded screening for HIV is unknown in the era of effective antiretroviral therapy. We developed a computer simulation model of HIV screening and treatment to compare routine, voluntary HIVCTR with current practice in three target populations: "high-risk" (3.0 percent prevalence of undiagnosed HIV infection; 1.2 percent annual incidence); "CDC threshold" (1.0 percent and 0.12 percent, respectively); and "U.S. general" (0.1 percent and 0.01 percent). Input data were derived from clinical trials and observational cohorts. Outcomes included quality-adjusted survival, cost, and cost-effectiveness. In the high-risk population, the addition of one-time screening for HIV antibodies with an enzyme-linked immunosorbent assay (ELISA) to current practice was associated with earlier diagnosis of HIV (mean CD4 cell count at diagnosis, 210 vs. 154 per cubic millimeter). One-time screening also improved average survival time among HIV-infected patients (quality-adjusted survival, 220.7 months vs. 219.8 months). The incremental cost-effectiveness was 36,000 dollars per quality-adjusted life-year gained. Testing every five years cost 50,000 dollars per quality-adjusted life-year gained, and testing every three years cost 63,000 dollars per quality-adjusted life-year gained. In the CDC threshold population, the cost-effectiveness ratio for one-time screening with ELISA was 38,000 dollars per quality-adjusted life-year gained, whereas testing every five years cost 71,000 dollars per quality-adjusted life-year gained, and testing every three years cost 85,000 dollars per quality-adjusted life-year gained. In the U.S. general population, one-time screening cost 113,000 dollars per quality-adjusted life-year gained. In all but the lowest-risk populations, routine, voluntary screening for HIV once every three to five years is justified on both clinical and cost-effectiveness grounds. One-time screening in the general population may also be cost-effective. Copyright 2005 Massachusetts Medical Society.
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              The cost effectiveness of combination antiretroviral therapy for HIV disease.

              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. 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. 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 $45,460 to $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 $23,000. On the basis of additional data from other major studies, the cost-effectiveness ratio for three-drug therapy ranged from $13,000 to $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. Treatment of HIV infection with a combination of three antiretroviral drugs is a cost-effective use of resources.
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                Author and article information

                Contributors
                Journal
                JMIR Res Protoc
                JMIR Res Protoc
                ResProt
                JMIR Research Protocols
                JMIR Publications (Toronto, Canada )
                1929-0748
                April 2019
                16 April 2019
                : 8
                : 4
                : e9898
                Affiliations
                [1 ] Division of General Academic Pediatrics Massachusetts General Hospital Boston, MA United States
                [2 ] Medical Practice Evaluation Center Massachusetts General Hospital Boston, MA United States
                [3 ] Department of Epidemiology and Center for Biostatistics in AIDS Research Harvard T.H. Chan School of Public Health Boston, MA United States
                [4 ] Departments of Pediatric and Adult Infectious Diseases Johns Hopkins University School of Medicine Baltimore, MD United States
                [5 ] Division of Infectious Diseases Department of Medicine Massachusetts General Hospital Boston, MA United States
                [6 ] University of Michigan School of Public Health Ann Arbor, MI United States
                [7 ] Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles, CA United States
                [8 ] St. Jude's Children's Research Hospital Memphis, TN United States
                [9 ] Division of Epidemiology and Community Health School of Public Health University of Minnesota Minneapolis, MN United States
                [10 ] Department of Epidemiology Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel Hill, NC United States
                [11 ] Hunter College of the City University of New York New York, NY United States
                [12 ] Institute for Global Health & Infectious Diseases University of North Carolina at Chapel Hill Chapel Hill, NC United States
                [13 ] Arnold School of Public Health University of South Carolina Columbia, SC United States
                [14 ] Center for Translational Behavioral Research Florida State University Tallahassee, FL United States
                [15 ] State University of New York Stony Brook, NY United States
                [16 ] Medical University of South Carolina Charleston, SC United States
                [17 ] Hackensack Meridian School of Medicine at Seton Hall University Nutley, NJ United States
                [18 ] Division of General Internal Medicine Massachusetts General Hospital Boston, MA United States
                [19 ] Department of Health Policy and Management Harvard T.H. Chan School of Public Health Boston, MA United States
                [20 ] Department of Biostatistics Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel Hill, NC United States
                Author notes
                Corresponding Author: Anne M Neilan aneilan@ 123456partners.org
                Author information
                http://orcid.org/0000-0001-7915-4974
                http://orcid.org/0000-0003-0002-681X
                http://orcid.org/0000-0002-8449-222X
                http://orcid.org/0000-0001-6920-6080
                http://orcid.org/0000-0002-4458-6934
                http://orcid.org/0000-0002-6150-2181
                http://orcid.org/0000-0002-6639-5231
                http://orcid.org/0000-0001-7569-2839
                http://orcid.org/0000-0001-7980-9846
                http://orcid.org/0000-0002-0148-2852
                http://orcid.org/0000-0002-2421-923X
                http://orcid.org/0000-0002-5555-9034
                http://orcid.org/0000-0001-6369-4685
                http://orcid.org/0000-0003-1665-7253
                http://orcid.org/0000-0002-6875-7566
                http://orcid.org/0000-0002-1713-0632
                http://orcid.org/0000-0001-7892-5327
                http://orcid.org/0000-0001-6471-7930
                http://orcid.org/0000-0002-7283-7200
                http://orcid.org/0000-0002-9106-4194
                http://orcid.org/0000-0002-4268-3263
                Article
                v8i4e9898
                10.2196/resprot.9898
                6488956
                30990464
                7e7908f7-c923-4778-aaf5-bec09f5c44c2
                ©Anne M Neilan, Kunjal Patel, Allison L Agwu, Ingrid V Bassett, K Rivet Amico, Catherine M Crespi, Aditya H Gaur, Keith J Horvath, Kimberly A Powers, H Jonathon Rendina, Lisa B Hightow-Weidman, Xiaoming Li, Sylvie Naar, Sharon Nachman, Jeffrey T Parsons, Kit N Simpson, Bonita F Stanton, Kenneth A Freedberg, Audrey C Bangs, Michael G Hudgens, Andrea L Ciaranello. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 16.04.2019.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org.as well as this copyright and license information must be included.

                History
                : 27 September 2018
                : 28 December 2018
                : 4 March 2019
                : 5 March 2019
                Categories
                Protocol
                Protocol

                adolescent,costs and cost analysis,health policy,hiv,medication adherence,modeling,retention in care,youth

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