title: How should we best estimate the mean recency duration for the BED method?
abstract: BED estimates of HIV incidence from cross-sectional surveys are obtained by restricting, to fixed time T, the period over which incidence is estimated. The appropriate mean recency duration (Ω(T)) then refers to the time where BED optical density (OD) is less than a pre-set cut-off C, given the patient has been HIV positive for at most time T. Five methods, tested using data for postpartum women in Zimbabwe, provided similar estimates of Ω(T) for C = 0.8: i) The ratio (r/s) of the number of BED-recent infections to all seroconversions over T = 365 days: 192 days [95% CI 168-216]. ii) Linear mixed modeling (LMM): 191 days [95% CI 174-208]. iii) Non-linear mixed modeling (NLMM): 196 days [95% CrI 188-204]. iv) Survival analysis (SA): 192 days [95% CI 168-216]. Graphical analysis: 193 days. NLMM estimates of Ω(T)--based on a biologically more appropriate functional relationship than LMM--resulted in best fits to OD data, the smallest variance in estimates of VT, and best correspondence between BED and follow-up estimates of HIV incidence, for the same subjects over the same time period. SA and NLMM produced very similar estimates of Ω(T) but the coefficient of variation of the former was .3 times as high. The r/s method requires uniformly distributed seroconversion events but is useful if data are available only from a single follow-up. The graphical method produces the most variable results, involves unsound methodology and should not be used to provide estimates of Ω(T). False-recent rates increased as a quadratic function of C: for incidence estimation C should thus be chosen as small as possible, consistent with an adequate resultant number of recent cases, and accurate estimation of Ω(T). Inaccuracies in the estimation of Ω(T) should not now provide an impediment to incidence estimation.
authors.affiliation: The South African Department of Science and Technology/National Research Foundation Centre of Excellence in Epidemiological Modelling and Analysis, University of Stellenbosch, Stellenbosch, South Africa. jhargrove@sun.ac.za
authors.forename: John
authors.initials: J
authors.lastname: Hargrove
authors.forename: Hayden
authors.initials: H
authors.lastname: Eastwood
authors.forename: Guy
authors.initials: G
authors.lastname: Mahiane
authors.forename: Cari
authors.initials: C
authors.lastname: van Schalkwyk
mesh_terms: D003430:Cross-Sectional Studies; D005260:Female; D005500:Follow-Up Studies; D015658:HIV Infections / Q000175:diagnosis / Q000453:epidemiology*; D006679:HIV Seropositivity; D015497:HIV-1 / Q000276:immunology; D006801:Humans; D007124:Immunoenzyme Techniques / Q000379:methods; D015994:Incidence; D015233:Models, Statistical; D013997:Time Factors; D015030:Zimbabwe / Q000453:epidemiology