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Stat Commun Infect Dis. 2017 Mar;9(1). pii: 20160002. doi: 10.1515/scid-2016-0002.. Epub 2017 Mar 14.

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

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Department of Statistical Sciences, University of Cape Town, Rondebosch 7701, South Africa.
Stellenbosch University, The South African DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch, South Africa.
Medical Research Council, MRC Biostatistics Unit, Cambridge, United Kingdom of Great Britain and Northern Ireland.
Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA.
South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa.
Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA.
Department of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
Département des Maladies Infectieuses, Institut de Veille Sanitaire, Saint-Maurice, France.
Institut National de la Santé et de la Recherche Médicale - U1018, Centre de Recherche en Épidémiologie et Santé des Populations, Université Paris Sud, Le Kremlin Bicêtre, France.
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA USA.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.


The application of biomarkers for 'recent' infection in cross-sectional HIV incidence surveillance requires the estimation of critical biomarker characteristics. Various approaches have been employed for using longitudinal data to estimate the Mean Duration of Recent Infection (MDRI) - the average time in the 'recent' state. In this systematic benchmarking of MDRI estimation approaches, a simulation platform was used to measure accuracy and precision of over twenty approaches, in thirty scenarios capturing various study designs, subject behaviors and test dynamics that may be encountered in practice. Results highlight that assuming a single continuous sojourn in the 'recent' state can produce substantial bias. Simple interpolation provides useful MDRI estimates provided subjects are tested at regular intervals. Regression performs the best - while 'random effects' describe the subject-clustering in the data, regression models without random effects proved easy to implement, stable, and of similar accuracy in scenarios considered; robustness to parametric assumptions was improved by regressing 'recent'/'non-recent' classifications rather than continuous biomarker readings. All approaches were vulnerable to incorrect assumptions about subjects' (unobserved) infection times. Results provided show the relationships between MDRI estimation performance and the number of subjects, inter-visit intervals, missed visits, loss to follow-up, and aspects of biomarker signal and noise.


HIV; biomarkers for recent infection; cross-sectional incidence surveys; duration of recent infection; incidence estimation

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