Mean Duration Of Recent Infection Research Articles

Short Communication: Heightened HIV Antibody Responses in Postpartum Women as Exemplified by Recent Infection Assays: Implications for Incidence Estimates.

Laboratory assays that identify recent HIV infections are important for assessing impacts of interventions aimed at reducing HIV incidence. Kinetics of HIV humoral responses can vary with inherent assay properties, and between HIV subtypes, populations, and physiological states. They are important in determining mean duration of recent infection (MDRI) for antibody-based assays for detecting recent HIV infections. We determined MDRIs for multi-subtype peptide representing subtypes B, E and D (BED)-capture enzyme immunoassay, limiting antigen (LAg), and Bio-Rad Avidity Incidence (BRAI) assays for 101 seroconverting postpartum women, recruited in Harare from 1997 to 2000 during the Zimbabwe Vitamin A for Mothers and Babies trial, comparing them against published MDRIs estimated from seroconverting cases in the general population. We also compared MDRIs for women who seroconverted either during the first 9 months, or at later stages, postpartum. At cutoffs (C) of 0.8 for BED, 1.5 for LAg, and 40% for BRAI, estimated MDRIs for postpartum mothers were 192, 104, and 144 days, 33%, 32%, and 52% lower than published estimates of 287, 152 and 298 days, respectively, for clade C samples from general populations. Point estimates of MDRI values were 7%-19% shorter for women who seroconverted in the first 9 months postpartum than for those seroconverting later. MDRI values for three HIV incidence biomarkers are longer in the general population than among postpartum women, particularly those who recently gave birth, consistent with heightened immunological activation soon after birth. Our results provide a caution that MDRI may vary significantly between subjects in different physiological states.

Cross-Sectional HIV Incidence Surveillance: A Benchmarking of Approaches for Estimating the ‘Mean Duration of Recent Infection’

Abstract The application of biomarkers for ‘recent’ infection in cross-sectional HIV incidence surveillance requires the estimation of critical biomarker characteristics. Various approaches have been employed for using longitudinal data to estimate the Mean Duration of Recent Infection (MDRI) – the average time in the ‘recent’ state. In this systematic benchmarking of MDRI estimation approaches, a simulation platform was used to measure accuracy and precision of over twenty approaches, in thirty scenarios capturing various study designs, subject behaviors and test dynamics that may be encountered in practice. Results highlight that assuming a single continuous sojourn in the ‘recent’ state can produce substantial bias. Simple interpolation provides useful MDRI estimates provided subjects are tested at regular intervals. Regression performs the best – while ‘random effects’ describe the subject-clustering in the data, regression models without random effects proved easy to implement, stable, and of similar accuracy in scenarios considered; robustness to parametric assumptions was improved by regressing ‘recent’/‘non-recent’ classifications rather than continuous biomarker readings. All approaches were vulnerable to incorrect assumptions about subjects’ (unobserved) infection times. Results provided show the relationships between MDRI estimation performance and the number of subjects, inter-visit intervals, missed visits, loss to follow-up, and aspects of biomarker signal and noise.

Cross-Sectional HIV Incidence Surveillance: A Benchmarking of Approaches for Estimating the 'Mean Duration of Recent Infection'.

The application of biomarkers for 'recent' infection in cross-sectional HIV incidence surveillance requires the estimation of critical biomarker characteristics. Various approaches have been employed for using longitudinal data to estimate the Mean Duration of Recent Infection (MDRI) - the average time in the 'recent' state. In this systematic benchmarking of MDRI estimation approaches, a simulation platform was used to measure accuracy and precision of over twenty approaches, in thirty scenarios capturing various study designs, subject behaviors and test dynamics that may be encountered in practice. Results highlight that assuming a single continuous sojourn in the 'recent' state can produce substantial bias. Simple interpolation provides useful MDRI estimates provided subjects are tested at regular intervals. Regression performs the best - while 'random effects' describe the subject-clustering in the data, regression models without random effects proved easy to implement, stable, and of similar accuracy in scenarios considered; robustness to parametric assumptions was improved by regressing 'recent'/'non-recent' classifications rather than continuous biomarker readings. All approaches were vulnerable to incorrect assumptions about subjects' (unobserved) infection times. Results provided show the relationships between MDRI estimation performance and the number of subjects, inter-visit intervals, missed visits, loss to follow-up, and aspects of biomarker signal and noise.

Mean Recency Period for Estimation of HIV-1 Incidence with the BED-Capture EIA and Bio-Rad Avidity in Persons Diagnosed in the United States with Subtype B.

HIV incidence estimates are used to monitor HIV-1 infection in the United States. Use of laboratory biomarkers that distinguish recent from longstanding infection to quantify HIV incidence rely on having accurate knowledge of the average time that individuals spend in a transient state of recent infection between seroconversion and reaching a specified biomarker cutoff value. This paper describes five estimation procedures from two general statistical approaches, a survival time approach and an approach that fits binomial models of the probability of being classified as recently infected, as a function of time since seroconversion. We compare these procedures for estimating the mean duration of recent infection (MDRI) for two biomarkers used by the U.S. National HIV Surveillance System for determination of HIV incidence, the Aware BED EIA HIV-1 incidence test (BED) and the avidity-based, modified Bio-Rad HIV-1/HIV-2 plus O ELISA (BRAI) assay. Collectively, 953 specimens from 220 HIV-1 subtype B seroconverters, taken from 5 cohorts, were tested with a biomarker assay. Estimates of MDRI using the non-parametric survival approach were 198.4 days (SD 13.0) for BED and 239.6 days (SD 13.9) for BRAI using cutoff values of 0.8 normalized optical density and 30%, respectively. The probability of remaining in the recent state as a function of time since seroconversion, based upon this revised statistical approach, can be applied in the calculation of annual incidence in the United States.

Development and Evaluation of a Modified Fourth-Generation Human Immunodeficiency Virus Enzyme Immunoassay for Cross-Sectional Incidence Estimation in Clade B Populations.

Accurate methods for cross-sectional incidence estimation are needed for HIV surveillance and prevention research. We developed an avidity assay based on the fourth-generation Genetic Systems HIV Combo Ag/Ab EIA (Bio-Rad Combo assay) and evaluated its performance. The Bio-Rad Combo assay was modified incubating samples with and without 0.025 M diethylamine (DEA). The avidity index (AI) was calculated as the ratio of the DEA-treated to untreated result for a specific sample. We analyzed 2,140 samples from 808 individuals from the United States with known duration of HIV infection. The mean duration of recent infection (MDRI) and the false-recent rate (FRR, fraction of samples from individuals known to be infected >2 years misclassified as recent) were calculated for AI cutoffs of 20%-90% for the avidity assay alone and in combination with a viral load assay (VL, limit of detection 400 copies/ml). Factors associated with misclassification of samples collected ≥2 years after infections were also evaluated. The MDRI for the Bio-Rad Combo Avidity assay ranged from 50 days using an AI cutoff of 20% to 276 days using an AI cutoff of 90%; the FRR ranged from 0% to 9%. When samples with a VL <400 copies/ml were classified as nonrecent, the FRRs were reduced approximately twofold and the MDRI estimates were reduced by ∼20%. An AI cutoff of 50% provided an MDRI of 135 days with an FRR of 2.1%. All samples from elite suppressors had an AI >80%. In adjusted analysis, viral suppression and low CD4 cell count were significantly associated with misclassification among individuals infected >2 years. This modified Bio-Rad Combo Avidity assay may be a useful tool for cross-sectional HIV incidence estimation. Further research is needed to evaluate use of this assay in combination with other assays to accurately estimate population-level HIV incidence.

Recalibration of the limiting antigen avidity EIA to determine mean duration of recent infection in divergent HIV-1 subtypes.

BackgroundMean duration of recent infection (MDRI) and misclassification of long-term HIV-1 infections, as proportion false recent (PFR), are critical parameters for laboratory-based assays for estimating HIV-1 incidence. Recent review of the data by us and others indicated that MDRI of LAg-Avidity EIA estimated previously required recalibration. We present here results of recalibration efforts using >250 seroconversion panels and multiple statistical methods to ensure accuracy and consensus.MethodsA total of 2737 longitudinal specimens collected from 259 seroconverting individuals infected with diverse HIV-1 subtypes were tested with the LAg-Avidity EIA as previously described. Data were analyzed for determination of MDRI at ODn cutoffs of 1.0 to 2.0 using 7 statistical approaches and sub-analyzed by HIV-1 subtypes. In addition, 3740 specimens from individuals with infection >1 year, including 488 from patients with AIDS, were tested for PFR at varying cutoffs.ResultsUsing different statistical methods, MDRI values ranged from 88–94 days at cutoff ODn = 1.0 to 177–183 days at ODn = 2.0. The MDRI values were similar by different methods suggesting coherence of different approaches. Testing for misclassification among long-term infections indicated that overall PFRs were 0.6% to 2.5% at increasing cutoffs of 1.0 to 2.0, respectively. Balancing the need for a longer MDRI and smaller PFR (<2.0%) suggests that a cutoff ODn = 1.5, corresponding to an MDRI of 130 days should be used for cross-sectional application. The MDRI varied among subtypes from 109 days (subtype A&D) to 152 days (subtype C).ConclusionsBased on the new data and revised analysis, we recommend an ODn cutoff = 1.5 to classify recent and long-term infections, corresponding to an MDRI of 130 days (118–142). Determination of revised parameters for estimation of HIV-1 incidence should facilitate application of the LAg-Avidity EIA for worldwide use.

Comparison of antibody responses to HIV infection in Ugandan women infected with HIV subtypes A and D.

We compared the serologic response to HIV infection in Ugandan women with HIV subtype A (N=82) and D (N=32) infection using a limiting antigen avidity assay (LAg-Avidity assay); 2,614 samples were analyzed. Study participants were followed a median of 6.6 years after HIV seroconversion. Samples were classified as assay positive if they had a LAg-Avidity assay result <1.5 normalized optical density units (OD-n). Women with subtype D infection were more likely to have delayed antibody maturation. During the first 2 years after seroconversion, the mean time that women had an assay-positive result (mean duration of recent infection, MDRI) was longer for women with subtype D infection than women with subtype A infection (267.9 days, 95% CI: 231.2-308.2 vs. 167.3 days, 95% CI: 151.8-185.9 days, p<0.01). The MDRI was also longer for women with subtype D infection after excluding low viral load samples and samples from women on antiretroviral therapy (ART). Women infected for >2 years were also more likely to be misclassified as recently infected in they had subtype D infection. Women with subtype D infection were also more likely to have antibody waning compared to women with subtype A infection. These findings may be related to the higher pathogenicity of subtype D HIV infection and are relevant to use of the LAg-Avidity assay for cross-sectional HIV incidence estimation in populations where subtype D infection is prevalent.

Incorporating incidence information within the UNAIDS Estimation and Projection Package framework: a study based on simulated incidence assay data.

Objectives:The objective of this study is to extend the UNAIDS incidence estimation model, the UNAIDS Estimation and Projection Package (EPP), so that it can incorporate data from incidence assays.Methods:We propose combining the likelihood of the incidence assay data with the likelihood of other data, in a manner that is consistent with the biomarker-based incidence estimator using incidence assay data. Two calibrating parameters specify the performance of the incidence assays: the false recent rate and the mean duration of recent infection. We then use synthetic data, based on prevalence data obtained from antenatal clinic surveillances, and in some cases household surveys, from 24 countries, to examine the impact of including incidence assay data, under circumstances wherein the incidence assay data imply the same or a different incidence rate as that inferred from the prevalence data alone, and wherein incorrect calibrating parameters for the incidence assay data are used.Results:Using incidence assay data, in addition to prevalence data, can improve estimate by narrowing uncertainty intervals in derived HIV incidence estimates, and by providing information on levels or trends in incidence that were not apparent in the prevalence data alone. However, the effect is relatively modest if the sample size of the incidence assay survey is small and results can be biased if the calibrating parameters for the incidence assay data are not known accurately.Conclusion:Incorporating information from incidence assays in the manner proposed has the potential to improve estimates. Further work will examine in more detail the circumstances under which the contribution of incidence assay data would be most valuable.

Short Communication: Defining Optimality of a Test for Recent Infection for HIV Incidence Surveillance

The estimation of HIV incidence from cross-sectional surveys using tests for recent infection has attracted much interest. It is increasingly recognized that the lack of high performance recent infection tests is hindering the implementation of this surveillance approach. With growing funding opportunities, test developers are currently trying to fill this gap. However, there is a lack of consensus and clear guidance for developers on the evaluation and optimization of candidate tests. A fundamental shift from conventional thinking about test performance is needed: away from metrics relevant in typical public health settings where the detection of a condition in individuals is of primary interest (sensitivity, specificity, and predictive values) and toward metrics that are appropriate when estimating a population-level parameter such as incidence (accuracy and precision). The inappropriate use of individual-level diagnostics performance measures could lead to spurious assessments and suboptimal designs of tests for incidence estimation. In some contexts, such as population-level application to HIV incidence, bias of estimates is essentially negligible, and all that remains is the maximization of precision. The maximization of the precision of incidence estimates provides a completely general criterion for test developers to assess and optimize test designs. Summarizing the test dynamics into the properties relevant for incidence estimation, high precision estimates are obtained when (1) the mean duration of recent infection is large, and (2) the false-recent rate is small. The optimal trade-off between these two test properties will produce the highest precision, and therefore the most epidemiologically useful incidence estimates.

Lower-Sensitivity and Avidity Modifications of the Vitros Anti-HIV 1+2 Assay for Detection of Recent HIV Infections and Incidence Estimation

Recent-infection testing assays/algorithms (RITAs) have been developed to exploit the titer and avidity of HIV antibody evolution following seroconversion for incidence estimation. The Vitros Anti-HIV 1+2 assay (Ortho-Clinical Diagnostics) was approved by the FDA to detect HIV-1 and HIV-2 infections. We developed a less-sensitive (LS) and an avidity-modified version of this assay to detect recent HIV infection. Seroconversion panels (80 subjects, 416 samples) were tested to calculate the mean duration of recent infection (MDR) for these assays. A panel from known long-term (2+ years) HIV-infected subjects on highly active antiretroviral therapy (HAART) (n = 134) and subjects with low CD4 counts (AIDS patients [n = 140]) was used to measure the false-recent rate (FRR) of the assays. Using a signal-to-cutoff ratio of 20 and the LS-Vitros assay gave a RITA MDR of 215 days (95% confidence interval [95% CI], ± 65 days) and using an avidity index (AI) of 0.6 gave an MDR of 170 days (± 44 days), while a combination of the two assays yielded a MDR of 146 days (± 38.6) and an FRR of 8%. Misclassifying subjects with known long-term infection as recently infected occurred in 14% of AIDS patients and 29% (95% CI, 22, 38) of HAART subjects and 3% (95% CI, 0.8, 7.2) and 42% (95% CI, 33, 51), respectively, for the LS- and avidity-modified Vitros assays, with a misclassification rate of 15% (95% CI, 11, 20) overall using a dual-assay algorithm. Both modified Vitros assays can be used to estimate the length of time since seroconversion and in calculations for HIV incidence. Like other RITAs, they are subject to high FRR in subjects on HAART or with AIDS.

A New General Biomarker-based Incidence Estimator

Estimating disease incidence from cross-sectional surveys, using biomarkers for "recent" infection, has attracted much interest. Despite widespread applications to HIV, there is currently no consensus on the correct handling of biomarker results classifying persons as "recently" infected long after the infections occurred. We derive a general expression for a weighted average of recent incidence that-unlike previous estimators-requires no particular assumption about recent infection biomarker dynamics or about the demographic and epidemiologic context. This is possible through the introduction of an explicit timescale T that truncates the period of averaging implied by the estimator. The recent infection test dynamics can be summarized into 2 parameters, similar to those appearing in previous estimators: a mean duration of recent infection and a false-recent rate. We identify a number of dimensionless parameters that capture the bias that arises from working with tractable forms of the resulting estimator and elucidate the utility of the incidence estimator in terms of the performance of the recency test and the population state. Estimation of test characteristics and incidence is demonstrated using simulated data. The observed confidence interval coverage of the test characteristics and incidence is within 1% of intended coverage. Biomarker-based incidence estimation can be consistently adapted to a general context without the strong assumptions of previous work about biomarker dynamics and epidemiologic and demographic history.