Literature DB >> 18775292

Principles and uses of HIV incidence estimation from recent infection testing--a review.

S Le Vu1, J Pillonel, Caroline Semaille, P Bernillon, Y Le Strat, L Meyer, J C Desenclos.   

Abstract

Since the 1990s, the development of laboratory-based methods has allowed to estimate incidence of human immunodeficiency virus (HIV) infections on single samples. The tests aim to differentiate recent from established HIV infection. Incidence estimates are obtained by using the relationship between prevalence, incidence and duration of recent infection. We describe the principle of the methods and typical uses of these tests to characterise recent infection and derive incidence. We discuss the challenges in interpreting estimates and we consider the implications for surveillance systems. Overall, these methods can add remarkable value to surveillance systems based on prevalence surveys as well as HIV case reporting.The assumptions that must be fulfilled to correctly interpret the estimates are mostly similar to those required in prevalence measurement. However, further research on the specific aspect of window period estimation is needed in order to generalise these methods in various population settings.

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Mesh:

Year:  2008        PMID: 18775292

Source DB:  PubMed          Journal:  Euro Surveill        ISSN: 1025-496X


  22 in total

1.  Short Communication: Defining optimality of a test for recent infection for HIV incidence surveillance.

Authors:  Reshma Kassanjee; Thomas A McWalter; Alex Welte
Journal:  AIDS Res Hum Retroviruses       Date:  2013-10-26       Impact factor: 2.205

2.  A new general biomarker-based incidence estimator.

Authors:  Reshma Kassanjee; Thomas A McWalter; Till Bärnighausen; Alex Welte
Journal:  Epidemiology       Date:  2012-09       Impact factor: 4.822

3.  Estimating HIV Incidence in Populations Using Tests for Recent Infection: Issues, Challenges and the Way Forward.

Authors:  Timothy D Mastro; Andrea A Kim; Timothy Hallett; Thomas Rehle; Alex Welte; Oliver Laeyendecker; Tom Oluoch; Jesus M Garcia-Calleja
Journal:  J HIV AIDS Surveill Epidemiol       Date:  2010-01-01

Review 4.  How can we better identify early HIV infections?

Authors:  Nora E Rosenberg; Christopher D Pilcher; Michael P Busch; Myron S Cohen
Journal:  Curr Opin HIV AIDS       Date:  2015-01       Impact factor: 4.283

5.  Trends of HIV-1 incidence with credible intervals in Sweden 2002-09 reconstructed using a dynamic model of within-patient IgG growth.

Authors:  Ethan Obie Romero-Severson; Cody Lee Petrie; Edward Ionides; Jan Albert; Thomas Leitner
Journal:  Int J Epidemiol       Date:  2015-07-10       Impact factor: 7.196

6.  Estimating HIV Incidence Using a Cross-Sectional Survey: Comparison of Three Approaches in a Hyperendemic Setting, Ndhiwa Subcounty, Kenya, 2012.

Authors:  Stéphanie Blaizot; Andrea A Kim; Clement Zeh; Benjamin Riche; David Maman; Kevin M De Cock; Jean-François Etard; René Ecochard
Journal:  AIDS Res Hum Retroviruses       Date:  2016-12-13       Impact factor: 2.205

7.  Cross-Sectional HIV Incidence Surveillance: A Benchmarking of Approaches for Estimating the 'Mean Duration of Recent Infection'.

Authors:  Reshma Kassanjee; Daniela De Angelis; Marian Farah; Debra Hanson; Jan Phillipus Lourens Labuschagne; Oliver Laeyendecker; Stéphane Le Vu; Brian Tom; Rui Wang; Alex Welte
Journal:  Stat Commun Infect Dis       Date:  2017-03-14

Review 8.  Current and future assays for identifying recent HIV infections at the population level.

Authors:  Joanna Smoleń-Dzirba; Tomasz J Wąsik
Journal:  Med Sci Monit       Date:  2011-05

9.  Seroconverting blood donors as a resource for characterising and optimising recent infection testing algorithms for incidence estimation.

Authors:  Reshma Kassanjee; Alex Welte; Thomas A McWalter; Sheila M Keating; Marion Vermeulen; Susan L Stramer; Michael P Busch
Journal:  PLoS One       Date:  2011-06-09       Impact factor: 3.240

10.  Towards estimation of HIV-1 date of infection: a time-continuous IgG-model shows that seroconversion does not occur at the midpoint between negative and positive tests.

Authors:  Helena Skar; Jan Albert; Thomas Leitner
Journal:  PLoS One       Date:  2013-04-16       Impact factor: 3.240

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