Open Access
December 2011 Modeling of the HIV infection epidemic in the Netherlands: A multi-parameter evidence synthesis approach
Stefano Conti, Anne M. Presanis, Maaike G. van Veen, Maria Xiridou, Martin C. Donoghoe, Annemarie Rinder Stengaard, Daniela De Angelis
Ann. Appl. Stat. 5(4): 2359-2384 (December 2011). DOI: 10.1214/11-AOAS488


Multi-parameter evidence synthesis (MPES) is receiving growing attention from the epidemiological community as a coherent and flexible analytical framework to accommodate a disparate body of evidence available to inform disease incidence and prevalence estimation. MPES is the statistical methodology adopted by the Health Protection Agency in the UK for its annual national assessment of the HIV epidemic, and is acknowledged by the World Health Organization and UNAIDS as a valuable technique for the estimation of adult HIV prevalence from surveillance data. This paper describes the results of utilizing a Bayesian MPES approach to model HIV prevalence in the Netherlands at the end of 2007, using an array of field data from different study designs on various population risk subgroups and with a varying degree of regional coverage. Auxiliary data and expert opinion were additionally incorporated to resolve issues arising from biased, insufficient or inconsistent evidence. This case study offers a demonstration of the ability of MPES to naturally integrate and critically reconcile disparate and heterogeneous sources of evidence, while producing reliable estimates of HIV prevalence used to support public health decision-making.


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Stefano Conti. Anne M. Presanis. Maaike G. van Veen. Maria Xiridou. Martin C. Donoghoe. Annemarie Rinder Stengaard. Daniela De Angelis. "Modeling of the HIV infection epidemic in the Netherlands: A multi-parameter evidence synthesis approach." Ann. Appl. Stat. 5 (4) 2359 - 2384, December 2011.


Published: December 2011
First available in Project Euclid: 20 December 2011

zbMATH: 1234.62140
MathSciNet: MR2907118
Digital Object Identifier: 10.1214/11-AOAS488

Keywords: Bayesian inference , bias adjustment , evidence synthesis , hierarchical models , HIV infection

Rights: Copyright © 2011 Institute of Mathematical Statistics

Vol.5 • No. 4 • December 2011
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