Augmented cross-sectional studies with abbreviated follow-up for estimating HIV incidence

Cross-sectional HIV incidence estimation based on a sensitive and less-sensitive test offers great advantages over the traditional cohort study. However, its use has been limited due to concerns about the false negative rate of the less-sensitive test, reflecting the phenomenon that some subjects may remain negative permanently on the less-sensitive test. Wang and Lagakos (2010, Biometrics 66, 864-874) propose an augmented cross-sectional design that provides one way to estimate the size of the infected population who remain negative permanently and subsequently incorporate this information in the cross-sectional incidence estimator. In an augmented cross-sectional study, subjects who test negative on the less-sensitive test in the cross-sectional survey are followed forward for transition into the nonrecent state, at which time they would test positive on the less-sensitive test. However, considerable uncertainty exists regarding the appropriate length of follow-up and the size of the infected population who remain nonreactive permanently to the less-sensitive test. In this article, we assess the impact of varying follow-up time on the resulting incidence estimators from an augmented cross-sectional study, evaluate the robustness of cross-sectional estimators to assumptions about the existence and the size of the subpopulation who will remain negative permanently, and propose a new estimator based on abbreviated follow-up time (AF). Compared to the original estimator from an augmented cross-sectional study, the AF estimator allows shorter follow-up time and does not require estimation of the mean window period, defined as the average time between detectability of HIV infection with the sensitive and less-sensitive tests. It is shown to perform well in a wide range of settings. We discuss when the AF estimator would be expected to perform well and offer design considerations for an augmented cross-sectional study with abbreviated follow-up.     

Cross‐sectional human immunodeficiency virus incidence estimation accounting for heterogeneity across communities

Accurate estimation of human immunodeficiency virus (HIV) incidence rates is crucial for the monitoring of HIV epidemics, the evaluation of prevention programs, and the design of prevention studies. Traditional cohort approaches to measure HIV incidence require repeatedly testing large cohorts of HIV‐uninfected individuals with an HIV diagnostic test (eg, enzyme‐linked immunosorbent assay) for long periods of time to identify new infections, which can be prohibitively costly, time‐consuming, and subject to loss to follow‐up. Cross‐sectional approaches based on the usual HIV diagnostic test and biomarkers of recent infection offer important advantages over standard cohort approaches, in terms of time, cost, and attrition. Cross‐sectional samples usually consist of individuals from different communities. However, small sample sizes limit the ability to estimate community‐specific incidence and existing methods typically ignore heterogeneity in incidence across communities. We propose a permutation test for the null hypothesis of no heterogeneity in incidence rates across communities, develop a random‐effects model to account for this heterogeneity and to estimate community‐specific incidence, and provide one way to estimate the coefficient of variation. We evaluate the performance of the proposed methods through simulation studies and apply them to the data from the National Institute of Mental Health Project ACCEPT, a phase 3 randomized controlled HIV prevention trial in Sub‐Saharan Africa, to estimate the overall and community‐specific HIV incidence rates.     

Estimating the distribution of a renewal process from times at which events from an independent process are detected

The analysis of length-biased data has been mostly limited to the interarrival interval of a renewal process covering a specific time point. Motivated by a surveillance problem, we consider a more general situation where this time point is random and related to a specific event, for example, status change or onset of a disease. We also consider the problem when additional information is available on whether the event intervals (interarrival intervals covering the random event) end within or after a random time period (which we call a window period) following the random event. Under the assumptions that the occurrence rate of the random event is low and the renewal process is independent of the random event, we provide formulae for the estimation of the distribution of interarrival times based on the observed event intervals. Procedures for testing the required assumptions are also furnished. We apply our results to human immunodeficiency virus (HIV) test data from public test sites in Seattle, Washington, where the random event is HIV infection and the window period is from the onset of HIV infection to the time at which a less sensitive HIV test becomes positive. Results show that the estimator of the intertest interval length distribution from event intervals ending within the window period is less biased than the estimator from all event intervals; the latter estimator is affected by right truncation. Finally, we discuss possible applications to estimating HIV incidence and analyzing length-biased samples with right or left truncated data.     

Estimation of the Distribution of Infection Times Using Longitudinal Serological Markers of HIV: Implications for the Estimation of HIV Incidence

Summary In the last decade, interest has been focused on human immunodeficiency virus (HIV) antibody assays and testing strategies that could distinguish recent infections from established infection in a single serum sample. Incidence estimates are obtained by using the relationship between prevalence, incidence, and duration of recent infection (window period). However, recent works demonstrated limitations of this approach due to the use of an estimated mean “window period.” We propose an alternative approach that consists in estimating the distribution of infection times based on serological marker values at the moment when the infection is first discovered. We propose a model based on the repeated measurements of virological markers of seroconversion for the marker trajectory. The parameters of the model are estimated using data from a cohort of HIV‐infected patients enrolled during primary infection. This model can be used for estimating the distribution of infection times for newly HIV diagnosed subjects reported in a HIV surveillance system. An approach is proposed for estimating HIV incidence from these results.     

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.     

Risk of HIV infection from transfusion with blood negative for HIV antibody in a west African city.

OBJECTIVE--To estimate the risk of infection with HIV (HIV 1 or HIV 2, or both) from transfusion of a screened unit of blood in a high prevalence area in west Africa. DESIGN--Retrospective cohort study for January-July 1991. SETTING--National Blood Transfusion Centre, Abidjan, Côte d''Ivoire. SUBJECTS--Repeat donors (5831 units of blood) and first time donors (5076 units) in the first five months of 1991. MAIN OUTCOME MEASURES--Prevalence and estimated incidence of HIV infection in repeat and first time donors; estimated rate of potentially infected, HIV antibody negative units; and rate of (false negative) potentially infected units assuming a laboratory test sensitivity of 99%. RESULTS--Overall HIV prevalence was 11.0% in first time donors and 2.1% in repeat donors. In the first seven months of 1991, 29 HIV antibody positive (27 HIV 1, 1 HIV 2, 1 dually reactive) donors with a seronegative unit of blood earlier in the year were identified; 26 had donated blood eight weeks or less before their estimated dates of seroconversion and may have been infectious (minimum rate 26/5831 (4.5/1000 potentially infected units)). Estimated incidence of infection in repeat donors was 1.2-2.5%. Laboratory test insensitivity would result in an estimated 1.1/1000 false negative units from first time donors and 0.2/1000 units from regular donors. The overall rate of potentially infected units (all donors, seroconversions, and errors) was estimated at 5.4-10.6/1000. CONCLUSIONS--The risk of HIV infection from a single unit of blood remains substantial (5.4-10.6/1000 units). To prevent infection from blood transfusion in areas of high incidence and prevalence of HIV all but absolutely essential transfusions should be avoided, and donors with low incidence of HIV infection should be selected.     

多重环介导核酸等温扩增技术检测血液中常见病原体

背景：血液安全性筛查是输血前必要检测项目。目前临床采用血清学检测技术,存在较长的检测窗口期,易产生假阴性检测结果,造成输血交叉感染。目的：建立多重环介导核酸等温扩增技术,实现在一管反应体系内同时检测四种病原体：乙肝病毒,丙肝病毒,艾滋病毒和梅毒螺旋体。方法：通过限制性酶切处理多重环介导核酸等温扩增产物,利用酶切产物的长度分析扩增产物的种类,从而分析待测样本中含有何种血液病原体。结果：检测164例临床样本,其检测结果可以通过琼脂糖电泳,聚丙烯酰胺凝胶电泳及芯片电泳分析,且均可实现对多重扩增产物的酶切片段进行区分和鉴别。结论：多重环介导核酸等温扩增技术可以同时单管检测多种待测血液病原体,可以为临床提高简单、快速、高灵敏和高特异的检测技术。     

BED estimates of HIV incidence: resolving the differences, making things simpler

Objective

Develop a simple method for optimal estimation of HIV incidence using the BED capture enzyme immunoassay.

Design

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.     

Estimating HIV Incidence Based on Combined Prevalence Testing

Summary .  Knowledge of incidence rates of HIV and other infectious diseases is important in evaluating the state of an epidemic as well as for designing interventional studies. Estimation of disease incidence from longitudinal studies can be expensive and time consuming. Alternatively, Janssen et al. (1998,  Journal of the American Medical Association   280, 42–48) proposed the estimation of HIV incidence at a single point in time based on the combined use of a standard and "detuned" antibody assay. This article frames the problem from a longitudinal perspective, from which the maximum likelihood estimator of incidence is determined and compared with the Janssen estimator. The formulation also allows estimation for general situations, including different batteries of tests among subjects, inclusion of covariates, and a comparative evaluation of different test batteries to help guide study design. The methods are illustrated with data from an HIV interventional trial and a seroprevalence survey recently conducted in Botswana.     

Ontological model of multi-agent Smart-system for predicting drug properties based on modified algorithms of artificial immune systems

Estimating HIV incidence is crucial for monitoring the epidemiology of this infection, planning screening and intervention campaigns, and evaluating the effectiveness of control measures. However, owing to the long and variable period from HIV infection to the development of AIDS and the introduction of highly active antiretroviral therapy, accurate incidence estimation remains a major challenge. Numerous estimation methods have been proposed in epidemiological modeling studies, and here we review commonly-used methods for estimation of HIV incidence. We review the essential data required for estimation along with the advantages and disadvantages, mathematical structures and likelihood derivations of these methods. The methods include the classical back-calculation method, the method based on CD4+ T-cell depletion, the use of HIV case reporting data, the use of cohort study data, the use of serial or cross-sectional prevalence data, and biomarker approach. By outlining the mechanistic features of each method, we provide guidance for planning incidence estimation efforts, which may depend on national or regional factors as well as the availability of epidemiological or laboratory datasets.     

HIV antibody assay that gave false negative results: multicentre collaborative study.

OBJECTIVE: To identify false negative results arising from the use of a commercial kit to detect antibody to HIV-1 and HIV-2 between July 1995 and March 1996. DESIGN: The 56 laboratories in the United Kingdom that were using the assay were asked to retrieve and retest specimens with an alternative assay for HIV-1 and HIV-2. Details of false negative results were obtained and these serum samples further investigated. SUBJECTS: 24,181 patients tested with the assay who were reported as being negative for HIV antibody. An additional 497 patients were confirmed as HIV positive with the assay. RESULTS: Serum samples of 20,973 of the patients were retested, and four patients were found to have had false negative results with the kit; three further patients were found to have had false negative results in the course of other laboratory testing. The seven patients with false negative results with the kit were of diverse risk group and HIV-1 subtype. Four had evidence of recent HIV infection. CONCLUSION: The commercial kit had a sensitivity of 99.2% (497/501), or less if the additional three patients with false negative results were taken into account.     

A statistical method for the estimation of window-period risk of transfusion-transmitted HIV in donor screening under non-steady state

Human immunodeficiency virus (HIV) can be transmitted by transfusion of blood even if the blood unit is test-negative for HIV. This is largely due to a time period following an infection, called the window period, during which antibodies against HIV are not detectable. Window-period risk refers to the probability for a test-negative blood unit to be infectious because of its donation during the window period. Estimation of window-period risk is important in public health for evaluating the safety of donated blood. The standard method for this estimation problem has been based on so-called incidence/window-period (IWP) models in which blood-donation and HIV-infection processes are assumed to be stochastically stationary and independent. Here we propose a new approach in which we relax this key assumption of the IWP models. We estimate window-period risk for each unit of donated blood using a given distribution of window-period risk. The proposed method utilizes the actual observed donation intervals including those of seroconversions, thereby relaxing the assumption that may not be met in practice. Bootstrap is used to compute confidence intervals without specifying the complex dynamics of the donation and infection processes. A simulation study illustrates the usefulness of the proposed method over the IWP method in scenarios where the IWP assumptions do not hold. A real application of the proposed method is presented using blood bank data from a province of northern Thailand. Advantages and limitations of the proposed method are discussed and compared with the IWP models.     

A new pattern-based method for identifying recent HIV-1 infections from the viral <Emphasis Type="Italic">env</Emphasis> sequence

The long asymptomatic stage of HIV infection poses a great challenge in identifying recent HIV infections. This is a bottleneck for monitoring HIV epidemic trends and evaluating the effectiveness of national AIDS control programs. Several serological methods were used to address this issue with some success. Because of high false-positive rates in patients with advanced infection or in ART treatment, UNAIDS still hesitates to recommend their use in routine surveillance. We developed a new pattern-based method for measuring intra-patient viral genetic diversity for determination of recent infections and estimation of population incidence. This method is verified by using several datasets (424 subtype B and 77 CRF07_BC samples) with clearly identified HIV-1 infection times. Pattern-based diversities of recent infections are significantly lower than that of chronic ones. With larger window periods varying from 200 to 350 days, a higher accuracy (90%–95%) not affected by advanced disease nor ART treatment could be obtained. The pattern-based genetic method is supplementary to the existing serology-based assays, both of which could be suitable for use in low and high epidemic regions, respectively.     

Use of a high resolution melting (HRM) assay to compare gag, pol, and env diversity in adults with different stages of HIV infection

Background

Cross-sectional assessment of HIV incidence relies on laboratory methods to discriminate between recent and non-recent HIV infection. Because HIV diversifies over time in infected individuals, HIV diversity may serve as a biomarker for assessing HIV incidence. We used a high resolution melting (HRM) diversity assay to compare HIV diversity in adults with different stages of HIV infection. This assay provides a single numeric HRM score that reflects the level of genetic diversity of HIV in a sample from an infected individual.

Methods

HIV diversity was measured in 203 adults: 20 with acute HIV infection (RNA positive, antibody negative), 116 with recent HIV infection (tested a median of 189 days after a previous negative HIV test, range 14–540 days), and 67 with non-recent HIV infection (HIV infected >2 years). HRM scores were generated for two regions in gag, one region in pol, and three regions in env.

Results

Median HRM scores were higher in non-recent infection than in recent infection for all six regions tested. In multivariate models, higher HRM scores in three of the six regions were independently associated with non-recent HIV infection.

Conclusions

The HRM diversity assay provides a simple, scalable method for measuring HIV diversity. HRM scores, which reflect the genetic diversity in a viral population, may be useful biomarkers for evaluation of HIV incidence, particularly if multiple regions of the HIV genome are examined.     

Blinded and unblinded sample size reestimation procedures for stepped‐wedge cluster randomized trials

The ability to accurately estimate the sample size required by a stepped‐wedge (SW) cluster randomized trial (CRT) routinely depends upon the specification of several nuisance parameters. If these parameters are misspecified, the trial could be overpowered, leading to increased cost, or underpowered, enhancing the likelihood of a false negative. We address this issue here for cross‐sectional SW‐CRTs, analyzed with a particular linear‐mixed model, by proposing methods for blinded and unblinded sample size reestimation (SSRE). First, blinded estimators for the variance parameters of a SW‐CRT analyzed using the Hussey and Hughes model are derived. Following this, procedures for blinded and unblinded SSRE after any time period in a SW‐CRT are detailed. The performance of these procedures is then examined and contrasted using two example trial design scenarios. We find that if the two key variance parameters were underspecified by 50%, the SSRE procedures were able to increase power over the conventional SW‐CRT design by up to 41%, resulting in an empirical power above the desired level. Thus, though there are practical issues to consider, the performance of the procedures means researchers should consider incorporating SSRE in to future SW‐CRTs.     

Biomarker-based HIV incidence in a community sample of men who have sex with men in Paris, France

BACKGROUND: Population-based estimates of HIV incidence in France have revealed that men who have sex with men (MSM) are the most affected population and contribute to nearly half of new infections each year. We sought to estimate HIV incidence among sexually active MSM in Paris gay community social venues. METHODOLOGY/ PRINCIPAL FINDINGS: A cross-sectional survey was conducted in 2009 in a sample of commercial venues such as bars, saunas and backrooms. We collected a behavioural questionnaire and blood sample. Specimens were tested for HIV infection and positive specimens then tested for recent infection by the enzyme immunoassay for recent HIV-1 infection (EIA-RI). We assessed the presence of antiretroviral therapy among infected individuals to rule out treated patients in the algorithm that determined recent infection. Biomarker-based cross-sectional incidence estimates were calculated. We enrolled 886 MSM participants among which 157 (18%) tested HIV positive. In positive individuals who knew they were infected, 75% of EIA-RI positive results were due to ART. Of 157 HIV positive specimens, 15 were deemed to be recently infected. The overall HIV incidence was estimated at 3.8% person-years (py) [95%CI: 1.5-6.2]. Although differences were not significant, incidence was estimated to be 3.5% py [0.1-6.1] in men having had a negative HIV test in previous year and 4.8% py [0.1-10.6] in men having had their last HIV test more than one year before the survey, or never tested. Incidence was estimated at 4.1% py [0-8.3] in men under 35 years and 2.5% py [0-5.4] in older men. CONCLUSIONS/ SIGNIFICANCE: This is the first community-based survey to estimate HIV incidence among MSM in France. It includes ART detection and reveals a high level of HIV transmission in sexually active individuals, despite a high uptake of HIV testing. These data call for effective prevention programs targeting MSM engaged in high-risk behaviours.     

A Comparison of Biomarker Based Incidence Estimators

Background

Cross-sectional surveys utilizing biomarkers that test for recent infection provide a convenient and cost effective way to estimate HIV incidence. In particular, the BED assay has been developed for this purpose. Controversy surrounding the way in which false positive results from the biomarker should be handled has lead to a number of different estimators that account for imperfect specificity. We compare the estimators proposed by McDougal et al., Hargrove et al. and McWalter & Welte.

Methodology/Principal Findings

The three estimators are analyzed and compared. An identity showing a relationship between the calibration parameters in the McDougal methodology is shown. When the three estimators are tested under a steady state epidemic, which includes individuals who fail to progress on the biomarker, only the McWalter/Welte method recovers an unbiased result.

Conclusions/Significance

Our analysis shows that the McDougal estimator can be reduced to a formula that only requires calibration of a mean window period and a long-term specificity. This allows simpler calibration techniques to be used and shows that all three estimators can be expressed using the same set of parameters. The McWalter/Welte method is applicable under the least restrictive assumptions and is the least prone to bias of the methods reviewed.     

Development of Methods for Cross-Sectional HIV Incidence Estimation in a Large,Community Randomized Trial

Background

Accurate methods of HIV incidence determination are critically needed to monitor the epidemic and determine the population level impact of prevention trials. One such trial, Project Accept, a Phase III, community-randomized trial, evaluated the impact of enhanced, community-based voluntary counseling and testing on population-level HIV incidence. The primary endpoint of the trial was based on a single, cross-sectional, post-intervention HIV incidence assessment.

Methods and Findings

Test performance of HIV incidence determination was evaluated for 403 multi-assay algorithms [MAAs] that included the BED capture immunoassay [BED-CEIA] alone, an avidity assay alone, and combinations of these assays at different cutoff values with and without CD4 and viral load testing on samples from seven African cohorts (5,325 samples from 3,436 individuals with known duration of HIV infection [1 month to >10 years]). The mean window period (average time individuals appear positive for a given algorithm) and performance in estimating an incidence estimate (in terms of bias and variance) of these MAAs were evaluated in three simulated epidemic scenarios (stable, emerging and waning). The power of different test methods to detect a 35% reduction in incidence in the matched communities of Project Accept was also assessed. A MAA was identified that included BED-CEIA, the avidity assay, CD4 cell count, and viral load that had a window period of 259 days, accurately estimated HIV incidence in all three epidemic settings and provided sufficient power to detect an intervention effect in Project Accept.

Conclusions

In a Southern African setting, HIV incidence estimates and intervention effects can be accurately estimated from cross-sectional surveys using a MAA. The improved accuracy in cross-sectional incidence testing that a MAA provides is a powerful tool for HIV surveillance and program evaluation.     

Letter to the editor: Commentary on the Fulani—History,genetics, and linguistics,an adjunct to Hassan et al., 2008

Many recent studies have used long bone cross‐sectional geometric properties in various comparative analyses. Methods have been described for reconstructing diaphyseal cross sections from external molds and biplanar radiographs that produce accurate results (within 5% of true values on average). The manual image processing required, however, is both time and labor intensive. A new freely available program developed here for the computational freeware, R, automates much of the process. This study compares cross‐sectional properties calculated using the new R program to those from peripheral quantitative CT (pQCT) and the original manual method. We find that the R program works aswell as the original manual image processing for most cross sections eliminates the chance for entry errors at several steps and greatly speeds up data collection. Am J Phys Anthropol 142:665–669, 2010. © 2010 Wiley‐Liss, Inc.     

A population-based study of Helicobacter pylori infection in Chinese children resident in Hong Kong: prevalence and potential risk factors

Background: Data of Helicobacter pylori prevalence in children and its risk factors provide clues to the health authority to estimate burden of H. pylori‐associated diseases usually encountered in adulthood and facilitate healthcare planning. Materials and Methods: A cross‐sectional population‐based study was conducted in Chinese children in elementary and high schools. Schools were selected from all three major areas of Hong Kong. H. pylori infection was defined by a positive ¹³C‐urea breath test. Study subjects were stratified into six age groups for estimation of prevalence. Potential risk factors were analyzed from data of self‐administered questionnaires. Results: A total of 2480 children (aged 6–19, male: 47.3%) participated in the study. Overall, 324 (13.1%) were positive for H. pylori. There was no difference in prevalence between sexes, and no statistical trend in the prevalence across the six age groups. Multivariate logistic regression identified lack of formal education of mother (OR = 2.43, 95%CI 1.36–4.34), family history of gastric cancer (OR = 2.19, 95%CI 1.09–4.41), and household member > 5 (OR = 1.57, 95%CI 1.12–2.19) to be positively associated with H. pylori infection in our children. Conclusions: The H. pylori prevalence of Hong Kong children is comparable to the data of developed countries. The association with family history of gastric cancer justifies further study to investigate the cost‐benefit of community screening program for such children to decrease the incidence of gastric cancer in adulthood.