title: BED estimates of HIV incidence: resolving the differences, making things simpler.
abstract: OBJECTIVE
  Develop a simple method for optimal estimation of HIV incidence using the BED capture enzyme immunoassay.
  
  
  METHODS
  Use existing BED data to estimate mean recency duration, false recency rates and HIV incidence with reference to a fixed time period, T.
  
  
  METHODS
  Compare 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.
  
  
  RESULTS
  Follow-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.
  
  
  CONCLUSIONS
  Referring 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.
authors.affiliation: Department of Science and Technology, National Research Foundation, Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), University of Stellenbosch, Stellenbosch, South Africa. jhargrove@sun.ac.za
authors.forename: John
authors.initials: J
authors.lastname: Hargrove
authors.forename: Cari
authors.initials: C
authors.lastname: van Schalkwyk
authors.forename: Hayden
authors.initials: H
authors.lastname: Eastwood
mesh_terms: D015415:Biomarkers / Q000032:analysis; D005500:Follow-Up Studies; D015658:HIV Infections / Q000453:epidemiology*; D015497:HIV-1 / Q000276:immunology / Q000472:pathogenicity; D007124:Immunoenzyme Techniques / Q000379:methods*; D007074:Immunoglobulin G / Q000032:analysis / Q000276:immunology; D015994:Incidence; D055620:Optical Phenomena; D013997:Time Factors
chemical_list: D015415:Biomarkers; D007074:Immunoglobulin G