Simple Estimation of Incident HIV Infection Rates in Notification Cohorts Based on Window Periods of Algorithms for Evaluation of Line-Immunoassay Result Patterns

Abstract

BackgroundTests 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.MethodsWe 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.ResultsWindow periods varied between 45.8 and 130.1 days and correlated well with the algorithms' diagnostic sensitivity (R2 = 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.ConclusionsIIR 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.

Decreasing Proportion of Recent Infections among Newly Diagnosed HIV-1 Cases in Switzerland, 2008 to 2013 Based on Line-Immunoassay-Based Algorithms

“…In a further study, we determined the window periods of all algorithms and calculated the IIR for the same four annual cohorts, but based on the relationship ‘Prevalence = Incidence x Duration’. The resulting IIRs and their changes were very comparable to those determined based on test performance [ 18 ].…”

“…Performance-based IIR (IIR P ) was calculated based on the relationship n tested incident = n true- incident + n false-incident , wherein n true-incident = n tested × IIR P × %Sensitivity/100 and n false-incident = n tested × (1 − IIR P ) × (1 − %Specificity/100). Therefore, as published previously [ 14 , 17 , 18 ], IIR P = (n tested incident / n tested + %Specificity/100 − 1) / (%Sensitivity/100 + %Specificity/100 − 1). Results below zero were set to zero.…”

“…Knowing now that adjustments for seeking early diagnosis (S 2 ) or for symptoms of PHI (S 3 ) reduce the numbers of incident infections to 54% and 76%, respectively, we applied these adjustments also to window-based incident infection estimates ( Fig 2 , panels on the right). This is justified, because a linear relationship between the diagnostic sensitivity of the algorithms and their window time has been demonstrated [ 18 ]. A proportional change in one of these two parameters is thus accompanied by an equal proportional change in the other.…”

“…In contrast to TRIs like the BED incidence assay or tests based on antibody affinity, which are solely based on signal/cutoff ratios, the Inno-Lia algorithms in addition define band patterns that differ between early and late infection. In early infection, reaction to p24 and p17 is generally stronger than to p31, because antibodies to p31 are the last to appear [ 18 , 31 , 32 ]. In advanced infection, however, antibody reaction to p31 and the envelope proteins is preserved, while that to p17 and p24 is diminished, due to an early down-regulation in patients with disease progression [ 26 – 30 ].…”

“…The window periods of the selected 10 best algorithms, defined as the duration of HIV-1 infection after which, according to a given algorithm, all samples would be classified as representing an older infection, were taken from [ 18 ] and are listed in S1 Table .…”