Abstract
BackgroundThe objectives of this study were to determine the capacity of BED incidence testing to a) estimate the effect of a HIV prevention intervention and b) provide adequate statistical power, when used among young people from sub-Saharan African settings with high HIV incidence rates.MethodsFirstly, after having elaborated plausible scenarios based on empirical data and the characteristics of the BED HIV-1 Capture EIA (BED) assay, we conducted statistical calculations to determine the BED theoretical power and HIV incidence rate ratio (IRR) associated with an intervention when using BED incidence testing. Secondly, we simulated a cross-sectional study conducted in a population among whom an HIV intervention was rolled out. Simulated data were analyzed using a log-linear Poisson model to recalculate the IRR and its confidence interval, and estimate the BED practical power. Calculations were conducted with and without corrections for misclassifications.ResultsCalculations showed that BED incidence testing can yield a BED theoretical power of 75% or more of the power that can be obtained in a classical cohort study conducted over a duration equal to the BED window period. Statistical analyses using simulated populations showed that the effect of a prevention intervention can be estimated with precision using classical statistical analysis of BED incidence testing data, even with an imprecise knowledge of the characteristics of the BED assay. The BED practical power was lower but of the same magnitude as the BED theoretical power.ConclusionsBED incidence testing can be applied to reasonably small samples to achieve good statistical power when used among young people to estimate IRR.
Highlights
Since the first detuned enzyme immunoassay to detect recent HIV seroconversion was described in 1998 [1], there has been great interest in the application of laboratory methods to measure HIV incidence rates from cross-sectional samples [2]
The objectives of this study were to determine the capacity of BED incidence testing to a) estimate the effect of a prevention intervention and b) provide adequate statistical power, when used among young people from sub-Saharan African settings with high HIV incidence rates
NC = not calculable NA = Not applicable CI = confidence interval. a bWeights k1 and k2 introduced in the Poisson log-linear model for each group a among HIV-positive tested recent seroconverters and b among HIV-positive tested long-term seroconverters. cFactor by which the incidence rate is multiplied in the intervention group in comparison with the control group. dThe BED theoretical power was obtained from Table 1 (0.62 for simulations 1 thru 7, 0.91 for simulations 8 thru 10, and 0.53 for simulation 11)
Summary
Since the first detuned enzyme immunoassay to detect recent HIV seroconversion was described in 1998 [1], there has been great interest in the application of laboratory methods to measure HIV incidence rates from cross-sectional samples [2]. The ability to reliably measure HIV incidence rate ratios (IRR) using cross-sectional data has vast public health importance in HIV surveillance and in prevention studies. Two of the current challenges in using HIV incidence assays to characterize HIV incidence rates are a) knowledge of the BED window period (i.e. time between first infection and when the test can reliably detect that infection) and b) misclassifications. The objectives of this study were to determine the capacity of BED incidence testing to a) estimate the effect of a HIV prevention intervention and b) provide adequate statistical power, when used among young people from subSaharan African settings with high HIV incidence rates
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