Abstract
ObjectiveDevelop a simple method for optimal estimation of HIV incidence using the BED capture enzyme immunoassay.DesignUse existing BED data to estimate mean recency duration, false recency rates and HIV incidence with reference to a fixed time period, T.MethodsCompare BED and cohort estimates of incidence referring to identical time frames. Generalize this approach to suggest a method for estimating HIV incidence from any cross-sectional survey.ResultsFollow-up and BED analyses of the same, initially HIV negative, cases followed over the same set time period T, produce estimates of the same HIV incidence, permitting the estimation of the BED mean recency period for cases who have been HIV positive for less than T. Follow-up of HIV positive cases over T, similarly, provides estimates of the false-recent rate appropriate for T. Knowledge of these two parameters for a given population allows the estimation of HIV incidence during T by applying the BED method to samples from cross-sectional surveys. An algorithm is derived for providing these estimates, adjusted for the false-recent rate. The resulting estimator is identical to one derived independently using a more formal mathematical analysis. Adjustments improve the accuracy of HIV incidence estimates. Negative incidence estimates result from the use of inappropriate estimates of the false-recent rate and/or from sampling error, not from any error in the adjustment procedure.ConclusionsReferring all estimates of mean recency periods, false-recent rates and incidence estimates to a fixed period T simplifies estimation procedures and allows the development of a consistent method for producing adjusted estimates of HIV incidence of improved accuracy. Unadjusted BED estimates of incidence, based on life-time recency periods, would be both extremely difficult to produce and of doubtful value.
Highlights
For infections of long duration, prevalence estimates are less informative than incidence as measures of the state and trajectory of an epidemic
One could calculate incidence from the samples collected in cross-sectional surveys used to estimate HIV prevalence – if it were possible to identify, from among the HIV positive cases, those that had become infected within some specified period prior to the time of the survey
In this paper we suggest a fresh approach, which resolves difficulties with the BED method and, more generally, provides a simple improved approach to HIV incidence estimation using biomarkers
Summary
For infections of long duration, prevalence estimates are less informative than incidence as measures of the state and trajectory of an epidemic. For HIV, where infection durations can exceed a decade even for patients not on antiretroviral therapy (ART) – and can be even longer for patients who are – incidence estimates are important. Whereas HIV prevalence is relatively easy to measure, HIV incidence is much more difficult. Even the so-called ‘‘gold standard’’ approach, involving follow-up of cohorts of initially HIV negative cases, is not without bias and is costly, time consuming and logistically challenging. One could calculate incidence from the samples collected in cross-sectional surveys used to estimate HIV prevalence – if it were possible to identify, from among the HIV positive cases, those that had become infected within some specified period prior to the time of the survey. A widely used approach is the BED Capture Enzyme Immuno-Assay (BED-CEIA or BED) assay which has been used alone, or in combination with an avidity assay, to estimate HIV incidence [1]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
