Published on March 15, 2012 by

BED Incidence Testing for Evaluating HIV Intervention Programs

The ability to estimate reliable HIV incidence rate ratios (IRRs) using cross-sectional data has vast public health importance in HIV surveillance and in prevention studies; it would reduce the need to recruit and maintain large and costly longitudinal cohorts. In fact, the most common method to evaluate HIV IRR is through cohort studies which are designed to estimate HIV incidence and the effects of interventions. However, the development of biomarkers which identify recently HIV infected individuals has made it possible to estimate HIV incidence using a cross-sectional survey. Following that, one study used classical statistical methods to analyse risk factors of recent HIV infection identified with a biomarker (1). It is therefore important to determine how that technology can be used to estimate incidence rate ratios.

BED testing for HIV incidence estimation

Brookmeyer and Quinn (2) were the first to propose the use of cross-sectional surveys to estimate HIV incidence, using a biomarker-based approach. Their method consisted of observing a biological marker indicating an immune system response to early infection, to classify individuals as either recently infected or non-recently infected and to use the biomarker results to estimate incidence.

Prior to the usage of a biomarker to estimate incidence, it is necessary to estimate the mean duration of the time individuals spend in the recently-infected state (“window period”). Unfortunately, the mean window period used in the publication quoted above is very short. This implies that unreasonably large sample sizes for the incidence cross-sectional surveys are needed to obtain accurate estimates. In order to improve the properties of the biomarkers, Janssen et al. (3) proposed a method based on the use of ‘detuned’ assays to detect recently infected individuals, which facilitates better precision in the incidence estimates because of a longer window period. The use of assays did not prove to be reliable because of subtype diversity which causes variability in immune response. The BED assay, which is a capture enzyme immunoassay (CEIA) based on protein sequences from the B, E and D HIV subtypes (4), was developed to improve biomarkers characteristics. However, in 2005, the UNAIDS Reference Group on Estimates, Modelling and Projections warned against the usage of BED to estimate HIV incidence rates. They called for the development of additional laboratory and modelling methodologies (5). However, currently the BED is the most widely used incidence assay and a systematic review of the BED incidence assay reported that it could produce accurate estimates of HIV incidence rates, if correct parameters were used (6).

Two of the current challenges in using HIV incidence assays to characterize HIV incidence rates are the knowledge of the BED window period and the misclassifications. The number of HIV-infected persons falsely identified as recent seroconverters, which is the main source of misclassification, depends on the proportion of HIV-positive participants whose infection duration exceeds the BED window period. This number, in absolute value, is lower among young people of a sub-Saharan African setting with high incidence rates, in comparison with populations with wider age ranges. In fact, in such settings, young people have been exposed to HIV for a short duration because of their recent onset of sexual activity and amongst them the fraction of HIV-positive individuals on antiretroviral drugs or having low CD4 count is lower. Thus, the BED incidence assay is optimised when it is used among young people of sub-Saharan African settings with high incidence rates.

Moreover, although the conventional cut-off value for the BED assay is 0.80, corresponding to a BED window period (W) of about six months, Fiamma et al. showed in an empirical study that higher cut-off values of up to 1.89, corresponding to a W of about 15 months, can be used among young people in South Africa (7).

The study further suggested that the BED incidence testing may be used to assess the effect of an intervention. More precisely, the authors demonstrated that the protective effect of male circumcision could have been calculated using only blood samples collected from participants at the last follow-up visit of the Orange Farm male circumcision trial (7).

BED testing for HIV incidence rate ratios estimation

The generalisation of this result was investigated in a recent study (8). The authors of that study examined the capacity of the BED incidence testing to estimate the effect of a prevention intervention and provided a tool to calculate statistical power, when used among young people of sub-Saharan Africa. They designed a study where a fraction of a population was offered an intervention aimed at reducing the effect of HIV acquisition. Then theoretical calculations were performed, which led to a formula giving the maximum likelihood estimator of the effect. Their formula provided an unbiased estimate of the effect when the population size was large. The theoretical power which they called “BED theoretical power” could then be derived by using either statistical tools or simulations. This was compared to the cohort power, which is the statistical power to detect a significant effect in the case of a classical cohort study with the same duration as the window period.

Because the BED theoretical power estimate obtained from their theoretical calculations is not usable in practice, the authors proposed a method to estimate HIV IRR from empirical data. They simulated random samples of individuals belonging to the intervention and control groups. Each individual was characterised by their time since sexual debut, HIV status and the variable indicating recent seroconversion status (Yes or No for those HIV-positive and NA – not applicable – for the others). Data from this simulated population were analysed using a Poisson-log-linear model to estimate the value of the intervention effect. The process was repeated 10 000 times and the “BED practical power” estimated using these simulations.

Numerical simulations were performed using values from published data. The baseline values for HIV incidence rates amongst men and women were 2.1% and 5%, respectively. The intervention was assumed to reduce the HIV incidence by 60% which corresponds to an effect of 0.4. In almost all the simulations, the size of each group was 1500 and the (maximum) duration of sexual activity was 6 years. The characteristics of the BED assay were assumed to be perfectly known in the simulations. These simulations showed that when the short-term specificity is equal to the sensitivity and equal to 0.87, and if the long-term specificity is equal to 0.96, then the BED theoretical power is 0.62 for men and 0.91 for women, which is 94% and 97% of the cohort power for men and women, respectively. They also showed that: a) the power increases with increasing HIV incidence rate, increasing BED window period, increasing sample size or increasing duration of the intervention and b) the power decreases with increasing time since sexual debut or decreasing intervention effect, decreasing specificitiy or sensitivity of the assay.

The practical estimations of the effect, which consisted of estimates obtained using a Poisson log-linear model, were relatively close to the true value. These estimates were the poorest when the characteristics of the assay were not known with great precision or when the Poisson model was not weighted. Results from these simulations also showed that the BED practical power for men and women are both roughly 75% of the BED theoretical power for men.

Finally, the method was applied to empirical data collected at the last follow-up visit of the Orange Farm male circumcision trial (9). The estimated effect was in strong agreement with the values obtained from classical statistical analysis. The effect was underestimated when not corrected for the long-term specificity and misclassification.

Overall, the study demonstrated the ability of the BED testing to a) reliably measure the effect of an HIV intervention aiming at reducing the HIV incidence rate, with classical analysis of individual data, correcting for misclassification and b) lead to a statistical power close to the power obtained in classical cohort studies conducted among samples of the same size as the cross-sectional survey. This suggests the need to change the assumption according to which HIV incidence assays are not of practical use because they require a very large sample size.