title: Simple estimation of incident HIV infection rates in notification cohorts based on window periods of algorithms for evaluation of line-immunoassay result patterns.
abstract: BACKGROUND
  Tests for recent infections (TRIs) are important for HIV surveillance. We have shown that a patient's antibody pattern in a confirmatory line immunoassay (Inno-Lia) also yields information on time since infection. We have published algorithms which, with a certain sensitivity and specificity, distinguish between incident (< = 12 months) and older infection. In order to use these algorithms like other TRIs, i.e., based on their windows, we now determined their window periods.
  
  
  METHODS
  We classified Inno-Lia results of 527 treatment-naïve patients with HIV-1 infection < = 12 months according to incidence by 25 algorithms. The time after which all infections were ruled older, i.e. the algorithm's window, was determined by linear regression of the proportion ruled incident in dependence of time since infection. Window-based incident infection rates (IIR) were determined utilizing the relationship 'Prevalence = Incidence x Duration' in four annual cohorts of HIV-1 notifications. Results were compared to performance-based IIR also derived from Inno-Lia results, but utilizing the relationship 'incident = true incident + false incident' and also to the IIR derived from the BED incidence assay.
  
  
  RESULTS
  Window periods varied between 45.8 and 130.1 days and correlated well with the algorithms' diagnostic sensitivity (R(2) = 0.962; P<0.0001). Among the 25 algorithms, the mean window-based IIR among the 748 notifications of 2005/06 was 0.457 compared to 0.453 obtained for performance-based IIR with a model not correcting for selection bias. Evaluation of BED results using a window of 153 days yielded an IIR of 0.669. Window-based IIR and performance-based IIR increased by 22.4% and respectively 30.6% in 2008, while 2009 and 2010 showed a return to baseline for both methods.
  
  
  CONCLUSIONS
  IIR estimations by window- and performance-based evaluations of Inno-Lia algorithm results were similar and can be used together to assess IIR changes between annual HIV notification cohorts.
authors.affiliation: University of Zurich, Institute of Medical Virology, Swiss National Center for Retroviruses, Zurich, Switzerland.
authors.forename: Jörg
authors.initials: J
authors.lastname: Schüpbach
authors.forename: Martin D
authors.initials: MD
authors.lastname: Gebhardt
authors.forename: Alexandra U
authors.initials: AU
authors.lastname: Scherrer
authors.forename: Leslie R
authors.initials: LR
authors.lastname: Bisset
authors.forename: Christoph
authors.initials: C
authors.lastname: Niederhauser
authors.forename: Stephan
authors.initials: S
authors.lastname: Regenass
authors.forename: Sabine
authors.initials: S
authors.lastname: Yerly
authors.forename: Vincent
authors.initials: V
authors.lastname: Aubert
authors.forename: Franziska
authors.initials: F
authors.lastname: Suter
authors.forename: Stefan
authors.initials: S
authors.lastname: Pfister
authors.forename: Gladys
authors.initials: G
authors.lastname: Martinetti
authors.forename: Corinne
authors.initials: C
authors.lastname: Andreutti
authors.forename: Thomas
authors.initials: T
authors.lastname: Klimkait
authors.forename: Marcel
authors.initials: M
authors.lastname: Brandenberger
authors.forename: Huldrych F
authors.initials: HF
authors.lastname: Günthard
mesh_terms: D000328:Adult; D000465:Algorithms; D015331:Cohort Studies; D005260:Female; D015658:HIV Infections / Q000175:diagnosis* / Q000453:epidemiology; D006801:Humans; D007118:Immunoassay / Q000379:methods*; D015994:Incidence; D008297:Male; D008403:Mass Screening / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013557:Switzerland / Q000453:epidemiology; D013997:Time Factors