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Open Access Research article

Can HIV incidence testing be used for evaluating HIV intervention programs? A reanalysis of the Orange Farm male circumcision trial (ANRS-1265)

Agnès Fiamma1, Pascale Lissouba2, Oliver E Amy34, Beverley Singh5, Oliver Laeyendecker34, Thomas C Quinn34, Dirk Taljaard6 and Bertran Auvert278*

Author Affiliations

1 Department of Medicine, University of California Los Angeles Program in Global Health, Johannesburg, South Africa

2 Institut National de la Santé et de la Recherche Médicale, INSERM U1018, Villejuif, 94804, France

3 Department of Medicine, Division of Infectious Diseases, The Johns Hopkins University School of Medicine, Baltimore, Maryland, 21205 USA

4 National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, 20892, USA

5 National Institute for Communicable Diseases, Sandringham, Johannesburg, 2131, South Africa

6 Progressus Research and Development, Northcliff, Johannesburg, 2115, South Africa

7 Hôpital Ambroise-Paré, Assistance Publique- Hôpitaux de Paris, Boulogne, 92100, France

8 University of Versailles, 78280, Guyancourt, France

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BMC Infectious Diseases 2010, 10:137  doi:10.1186/1471-2334-10-137

Published: 27 May 2010

Abstract

Background

The objective of this study was to estimate the effect of male circumcision (MC) on HIV acquisition estimated using HIV incidence assays and to compare it to the effect measured by survival analysis.

Methods

We used samples collected during the MC randomized controlled trial (ANRS-1265) conducted in Orange Farm (South Africa) among men aged 18 to 24. Among the 2946 samples collected at the last follow-up visit, 194 HIV-positive samples were tested using two incidence assays: Calypte HIV-EIA (BED) and an avidity assay based on the BioRad HIV1/2+O EIA (AI). The results of the assays were also combined (BED-AI). The samples included the 124 participants (4.2% of total) who were HIV-positive at randomization. The protective effect was calculated as one minus the intention-to-treat incidence rate ratio in an uncorrected manner and with correction for misclassifications, with simple theoretical formulae. Theoretical calculations showed that the uncorrected intention-to-treat effect was approximately independent of the value of the incidence assay window period and was the ratio of the number tested recent seroconverters divided by the number tested HIV-negative between the randomization groups. We used cut-off values ranging from 0.325 to 2.27 for BED, 31.6 to 96 for AI and 0.325-31.6 to 1.89-96 for BED-AI. Effects were corrected for long-term specificity using a previously published formula. 95% Confidence intervals (CI) were estimated by bootstrap resampling.

Results

With the highest cut-off values, the uncorrected protective effects evaluated by BED, AI and BED-AI were 50% (95%CI: 27% to 66%), 50% (21% to 69%) and 63% (36% to 81%). The corrections for misclassifications were lower than 50% of the number of tested recent. The corrected effects were 53% (30% to 70%), 55% (25% to 77%) and 67% (38% to 86%), slightly higher than the corresponding uncorrected values. These values were consistent with the previously reported protective effect of 60% (34% to 76%) obtained with survival analysis.

Conclusions

HIV incidence assays may be employed to assess the effect of interventions using cross-sectional data.