Cross-sectional HIV incidence estimation in HIV prevention research

Abstract

Accurate methods for estimating HIV incidence from cross-sectional samples would have great utility in prevention research. This report describes recent improvements in cross-sectional methods that significantly improve their accuracy. These improvements are based on the use of multiple biomarkers to identify recent HIV infections. These multiassay algorithms (MAAs) use assays in a hierarchical approach for testing that minimizes the effort and cost of incidence estimation. These MAAs do not require mathematical adjustments for accurate estimation of the incidence rates in study populations in the year before sample collection. MAAs provide a practical, accurate, and cost-effective approach for cross-sectional HIV incidence estimation that can be used for HIV prevention research and global epidemic monitoring.

Figure 1. Multi-assay algorithms for cross-sectional HIV incidence estimation in subtype B epidemics

Multi-assay algorithms (MAAs) were developed that combine serologic markers (the BED capture immunoassay [BED-CEIA] and an avidity assay) with non-serologic biomarkers (HIV viral load, with or without CD4 cell count) [10, 12]. In each MAA, assays are performed using a hierarchical approach (see text), with an optimal cut-off defined for each assay. CD4 cell count cut-offs are expressed as cells/mm³; BED-CEIA cut-offs are expressed as normalized optical density; avidity results are expressed as avidity index values (%); HIV viral load cut-offs are expressed as HIV RNA copies/mL. For each MAA, samples that meet the criteria for all assays are classified as MAA positive. Three MAAs are shown. MAA #1 is a four-assay MAA described in [10, 12] that was used to estimate incidence in three clinical studies (see Figure 2). MAA #2 is an alternate four-assay MAA described in [12] that maximizes the mean window period subject to the shadow being less than one year. MAA #3 is the three-assay MAA described in [12] that does not require CD4 cell count data and maximizes the mean window period subject to the shadow being less than one year. The mean window periods and shadows for all three of these MAAs were determined in a previous study [12] (see text); the 95% confidence intervals for each mean window period and shadow are shown in parentheses.

Figure 2. Comparison of cross-sectional incidence estimates and incidence observed from longitudinal follow-up of HIV-uninfected cohorts in three clinical studies performed in the United States

HIV incidence was evaluated in three clinical studies: the HIV Prevention Trials Network (HPTN) 064 study [52], the HIV Network for Prevention Trials (HIVNET) 001 Vaccine Preparedness study [53], and the HPTN 061 study [45]. Annual HIV incidence was determined in each study using two methods: longitudinal follow-up of HIV-uninfected individuals (FU, filled symbols) and cross-sectional analysis using a multi-assay algorithm (MAA #1 shown in Figure 1; CS, open symbols) [10, 11, 45]. Note that the mean window period of MAA #1 is based on the analysis reported in [10] using midpoint imputation for seroconversion times; a reanalysis using multiple imputations is reported in [12].