HIV incidence is the rate at which new HIV infections occur in populations. While HIV prevalence measures overall disease burden, HIV incidence tracks the leading edge of the HIV/AIDS epidemic. Accurate HIV incidence estimates are critical for monitoring the HIV/AIDS epidemic, identifying populations at high risk of HIV acquisition, targeting prevention efforts, and designing and evaluating HIV prevention trials. Current methods for cross-sectional HIV incidence determination are insufficiently accurate. Our goal is to develop and validate accurate, cost-effective methods for cross-sectional HIV incidence determination. Our hypothesis is that diverse laboratory assays and robust statistical modeling can be combined to improve the accuracy of cross-sectional HIV incidence estimates. We will focus on analysis of HIV incidence in both subtype B (the major subtype driving the HIV/AIDS epidemic in the United States) and subtype C (the major subtype driving the HIV/AIDS epidemic in sub-Saharan Africa);other subtypes prevalent in sub-Saharan Africa will also be analyzed.
The Specific Aims of the project are:
Aim 1 : Continue to build a repository of diverse, well-characterized samples with information on the duration of HIV infection. Analyze the samples using serologic HIV incidence assays.
Aim 2 : Further develop and validate a novel high resolution melting (HRM) assay for HIV diversity. Determine whether HIV diversity can be used as a biomarker to differentiate between individuals with recent vs. chronic HIV infection.
Aim 3 : Use statistical analysis and mathematical modeling to assess the accuracy of methods for HIV incidence determination. Apply those approaches to data from Aim 1 (CD4 cell count, HIV viral load, and data from serologic assays) and Aim 2 (data from the HRM assay) to identify methods for HIV incidence determination that perform well in a wide variety of settings, and to assess their relative costs. Our repository will include samples from at least 19 completed clinical trials, cohort studies, and research projects representing key geographic, demographic, and clinically-relevant populations. Over 10,000 of the samples are already in hand. We will integrate laboratory science with statistical analysis and mathematical modeling in all phases of the project, and will evaluate a broad range of design parameters, including use of individual assays versus multi-assay algorithms, use of different assay cutoffs, and sequential ordering of assays. We believe that this comprehensive approach will lead to identification of accurate and cost-effective methods for cross-sectional HIV incidence determination. ) )

Public Health Relevance

This project will evaluate and optimize methods that can be used to determine HIV incidence (the rate of new HIV infections) from cross sectional surveys of single blood samples collected from individuals. These methods are needed to monitor the HIV/AIDS epidemic, to identify populations at high risk of HIV infection, to target HIV prevention efforts, and to design and evaluate HIV prevention trials.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
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AIDS Clinical Studies and Epidemiology Study Section (ACE)
Program Officer
Sharma, Usha K
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Johns Hopkins University
Schools of Medicine
United States
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Kirkpatrick, Allison R; Patel, Eshan U; Celum, Connie L et al. (2016) Development and Evaluation of a Modified Fourth-Generation Human Immunodeficiency Virus Enzyme Immunoassay for Cross-Sectional Incidence Estimation in Clade B Populations. AIDS Res Hum Retroviruses 32:756-62
Solomon, Sunil Suhas; Mehta, Shruti H; McFall, Allison M et al. (2016) Community viral load, antiretroviral therapy coverage, and HIV incidence in India: a cross-sectional, comparative study. Lancet HIV 3:e183-90
Solomon, Sunil S; Mehta, Shruti H; Srikrishnan, Aylur K et al. (2015) High HIV prevalence and incidence among MSM across 12 cities in India. AIDS 29:723-31
Laeyendecker, Oliver; Redd, Andrew D; Nason, Martha et al. (2015) Antibody Maturation in Women Who Acquire HIV Infection While Using Antiretroviral Preexposure Prophylaxis. J Infect Dis 212:754-9
Ostrowski, Mario; Benko, Erika; Yue, Feng Yun et al. (2015) Intensifying Antiretroviral Therapy With Raltegravir and Maraviroc During Early Human Immunodeficiency Virus (HIV) Infection Does Not Accelerate HIV Reservoir Reduction. Open Forum Infect Dis 2:ofv138
Chen, Iris; Connor, Matthew B; Clarke, William et al. (2015) Antiretroviral Drug Use and HIV Drug Resistance Among HIV-Infected Black Men Who Have Sex With Men: HIV Prevention Trials Network 061. J Acquir Immune Defic Syndr 69:446-52
Longosz, Andrew F; Morrison, Charles S; Chen, Pai-Lien et al. (2015) Comparison of antibody responses to HIV infection in Ugandan women infected with HIV subtypes A and D. AIDS Res Hum Retroviruses 31:421-7
Konikoff, Jacob; Brookmeyer, Ron (2015) Sample size methods for estimating HIV incidence from cross-sectional surveys. Biometrics 71:1121-9
Marzinke, Mark A; Clarke, William; Wang, Lei et al. (2014) Nondisclosure of HIV status in a clinical trial setting: antiretroviral drug screening can help distinguish between newly diagnosed and previously diagnosed HIV infection. Clin Infect Dis 58:117-20
Huynh, Dat; Laeyendecker, Oliver; Brookmeyer, Ron (2014) A serial risk score approach to disease classification that accounts for accuracy and cost. Biometrics 70:1042-51

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