A multi-assay algorithm approach enables accurate estimation of HIV incidence from cross-sectional data

Development of Methods for Cross-Sectional HIV Incidence Estimation in a Large, Community Randomized Trial.

Laeyendecker O, Kulich M, Donnell D, Komárek A, Omelka M, Mullis CE, Szekeres G, Piwowar-Manning E, Fiamma A, Gray RH, Lutalo T, Morrison CS, Salata RA, Chipato T, Celum C, Kahle EM, Taha TE, Kumwenda NI, Karim QA, Naranbhai V, Lingappa JR, Sweat MD, Coates T, Eshleman SH. PLoS One. 2013 Nov 13;8(11):e78818. doi: 10.1371/journal.pone.0078818.

Background: Accurate methods of HIV incidence determination are critically needed to monitor the epidemic and determine the population level impact of prevention trials. One such trial, Project Accept, a Phase III, community-randomized trial, evaluated the impact of enhanced, community-based voluntary counseling and testing on population-level HIV incidence. The primary endpoint of the trial was based on a single, cross-sectional, post-intervention HIV incidence assessment.

Methods and findings: Test performance of HIV incidence determination was evaluated for 403 multi-assay algorithms [MAAs] that included the BED capture immunoassay [BED-CEIA] alone, an avidity assay alone, and combinations of these assays at different cutoff values with and without CD4 and viral load testing on samples from seven African cohorts (5,325 samples from 3,436 individuals with known duration of HIV infection [1 month to >10 years]). The mean window period (average time individuals appear positive for a given algorithm) and performance in estimating an incidence estimate (in terms of bias and variance) of these MAAs were evaluated in three simulated epidemic scenarios (stable, emerging and waning). The power of different test methods to detect a 35% reduction in incidence in the matched communities of Project Accept was also assessed. A MAA was identified that included BED-CEIA, the avidity assay, CD4 cell count, and viral load that had a window period of 259 days, accurately estimated HIV incidence in all three epidemic settings and provided sufficient power to detect an intervention effect in Project Accept.

Conclusions: In a Southern African setting, HIV incidence estimates and intervention effects can be accurately estimated from cross-sectional surveys using a MAA. The improved accuracy in cross-sectional incidence testing that a MAA provides is a powerful tool for HIV surveillance and program evaluation.

Abstract  Full-text [free] access

Editor’s notes: This study explores a cross-sectional method to determine occurrence of new HIV infections in a population. This was done as an alternative to longitudinal follow-up of individuals which is onerous; or using changes in estimates of existing infections as an indication of rates of new infections, which is potentially less accurate. The methods described here could be invaluable for monitoring the HIV epidemic and in evaluating prevention initiatives. Challenges in estimating HIV incidence include finding a suitable window period i.e., long enough in order to detect recent infection from a given sample, but not so long that chronic infections are also detected; and the impact of HIV viral load, treatment duration and other factors on serological assays. The investigators examined multiple approaches to combine existing tools in simulated epidemic scenarios. They validated their findings using samples with known duration of infection and available CD4 count blood samples, from a number of established HIV prevention trials. Testing algorithms that included multiple assays were superior to single serologic assays; the incidence estimates obtained using multiple assays had lower bias and better precision. The optimal method was a 4-assay multi-assay algorithm (MAA) which could detect the ratio between incidence in the intervention and control communities of Project Accept more accurately than would have been obtained with 6 monthly follow-up of a cohort of participants. The main limitation of note was that sub-type D infections were frequently misclassified. The authors recommend that further research for subtype D endemic areas is required. 

Epidemiology, HIV testing
Africa
Botswana, Kenya, Malawi, South Africa, Uganda, Zimbabwe
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