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. 2013 Feb 1;177(3):264-72.
doi: 10.1093/aje/kws436. Epub 2013 Jan 9.

Estimation of HIV incidence using multiple biomarkers

Ron Brookmeyer  1 , Jacob Konikoff, Oliver Laeyendecker, Susan H Eshleman
Affiliations

Estimation of HIV incidence using multiple biomarkers

Ron Brookmeyer et al. Am J Epidemiol. .

Abstract

The incidence of human immunodeficiency virus (HIV) is the rate at which new HIV infections occur in populations. The development of accurate, practical, and cost-effective approaches to estimation of HIV incidence is a priority among researchers in HIV surveillance because of limitations with existing methods. In this paper, we develop methods for estimating HIV incidence rates using multiple biomarkers in biological samples collected from a cross-sectional survey. An advantage of the method is that it does not require longitudinal follow-up of individuals. We use assays for BED, avidity, viral load, and CD4 cell count data from clade B samples collected in several US epidemiologic cohorts between 1987 and 2010. Considering issues of accuracy, cost, and implementation, we identify optimal multiassay algorithms for estimating incidence. We find that the multiple-biomarker approach to cross-sectional HIV incidence estimation corrects the significant deficiencies of currently available approaches and is a potentially powerful and practical tool for HIV surveillance.

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Figures

Figure 1.
Figure 1.
Mean window duration (µ) versus shadow (ψ) for algorithms with a shadow of less than 1 year.
Figure 2.
Figure 2.
Top-ranked algorithms for estimating human immunodeficiency virus (HIV) incidence using multiple biomarkers. In part A, the algorithm is based on 4 biomarkers (CD4 cell count, BED, avidity, and viral load); in part B, it is based on 3 biomarkers (BED, avidity, and viral load).
Figure 3.
Figure 3.
φ(t) for the algorithms shown in part A of Figure 1 (curve 1) and part B of Figure 1 (curve 2) and an algorithm using only the BED assay with a cutoff of 0.8 normalized optical density (curve 3) versus time since infection (i.e., seroconversion).
See this image and copyright information in PMC

References

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