Pooled nucleic acid testing increases the diagnostic yield of acute HIV infections in a high-risk population compared to 3rd and 4th generation HIV enzyme immunoassays

Capturing the pool dilution effect in group testing regression: A Bayesian approach

Group (pooled) testing is becoming a popular strategy for screening large populations for infectious diseases. This popularity is owed to the cost savings that can be realized through implementing group testing methods. These methods involve physically combining biomaterial (eg, saliva, blood, urine) collected on individuals into pooled specimens which are tested for an infection of interest. Through testing these pooled specimens, group testing methods reduce the cost of diagnosing all individuals under study by reducing the number of tests performed. Even though group testing offers substantial cost reductions, some practitioners are hesitant to adopt group testing methods due to the so-called dilution effect. The dilution effect describes the phenomenon in which biomaterial from negative individuals dilute the contributions from positive individuals to such a degree that a pool is incorrectly classified. Ignoring the dilution effect can reduce classification accuracy and lead to bias in parameter estimates and inaccurate inference. To circumvent these issues, we propose a Bayesian regression methodology which directly acknowledges the dilution effect while accommodating data that arises from any group testing protocol. As a part of our estimation strategy, we are able to identify pool specific optimal classification thresholds which are aimed at maximizing the classification accuracy of the group testing protocol being implemented. These two features working in concert effectively alleviate the primary concerns raised by practitioners regarding group testing. The performance of our methodology is illustrated via an extensive simulation study and by being applied to Hepatitis B data collected on Irish prisoners.

Generalized additive regression for group testing data

In screening applications involving low-prevalence diseases, pooling specimens (e.g., urine, blood, swabs, etc.) through group testing can be far more cost effective than testing specimens individually. Estimation is a common goal in such applications and typically involves modeling the probability of disease as a function of available covariates. In recent years, several authors have developed regression methods to accommodate the complex structure of group testing data but often under the assumption that covariate effects are linear. Although linearity is a reasonable assumption in some applications, it can lead to model misspecification and biased inference in others. To offer a more flexible framework, we propose a Bayesian generalized additive regression approach to model the individual-level probability of disease with potentially misclassified group testing data. Our approach can be used to analyze data arising from any group testing protocol with the goal of estimating multiple unknown smooth functions of covariates, standard linear effects for other covariates, and assay classification accuracy probabilities. We illustrate the methods in this article using group testing data on chlamydia infection in Iowa.

Incorporating the dilution effect in group testing regression

When screening for infectious diseases, group testing has proven to be a cost efficient alternative to individual level testing. Cost savings are realized by testing pools of individual specimens (eg, blood, urine, saliva, and so on) rather than by testing the specimens separately. However, a common concern that arises in group testing is the so-called "dilution effect." This occurs if the signal from a positive individual's specimen is diluted past an assay's threshold of detection when it is pooled with multiple negative specimens. In this article, we propose a new statistical framework for group testing data that merges estimation and case identification, which are often treated separately in the literature. Our approach considers analyzing continuous biomarker levels (eg, antibody levels, antigen concentrations, and so on) from pooled samples to estimate both a binary regression model for the probability of disease and the biomarker distributions for cases and controls. To increase case identification accuracy, we then show how estimates of the biomarker distributions can be used to select diagnostic thresholds on a pool-by-pool basis. Our proposals are evaluated through numerical studies and are illustrated using hepatitis B virus data collected on a prison population in Ireland.

Anti-HIV-1 antibodies based confirmatory results in Wuhan, China, 2012-2018

The number, intensity and order of emergence of HIV-1 specific antibodies in serum or plasma were associated with the stage of HIV-1 infection. In this study, we retrospectively analyzed the HIV-1 confirmatory results tested by western blot (WB) or recombination immunoblot assay (RIBA) in Wuhan, 2012-2018, to access the profiles of HIV-1 specific antibodies. A total of 14432 HIV-suspected serum or plasma samples collected from local hospitals and other HIV screening laboratories were further screened by two 4th generation enzyme-linked immunosorbent assay (ELISA) kits in our laboratory, of which 11068 specimens (76.69%) had at least one positive ELISA result and thereby were finally confirmed with WB or RIBA. RIBA had identified 652 (81.09%) positive and 13 (1.62%) indeterminate cases from July 1, 2014 to January 7, 2015, while WB had identified 8358 (81.43%) positive and 643 (6.26%) indeterminate cases in the other times during 2012-2018. The indeterminate rate of WB was significant higher than that of RIBA (p<0.001). Although the number of HIV-1 infected subjects increased significantly from 2012 (n = 911) to 2018 (n = 1578), the positive rate of HIV-1 antibodies decreased markedly from 70.08% in 2012 to 58.79% in 2018 (p<0.001). The most commonly observed antibody profile was gp160+gp120+p66+(p55+)p51+gp41+p31+p24+p17+ (4131, 49.43%) for WB-MP and gp160+gp120+gp41+p31+p24+p17+ (382, 58.59%) for RIBA-WANTAI, and the absence of reactivity to three possible serologic markers for recent HIV-1 infection, p31, p66, and p51, increased significantly from 2012 to 2018, with the overall rate of 17.03%, 9.40%, and 15.15%, respectively. The suspected acute HIV-1 infection was also observed to be increased in recent years, with an overall rate of 1.00%. Our results indicated the detection rate had decreased for HIV-1 infection, but increased for suspected recent and acute HIV-1 infection during 2012-2018, reflecting the efforts of intervention among high risk population.

From mixed effects modeling to spike and slab variable selection: A Bayesian regression model for group testing data

Due to reductions in both time and cost, group testing is a popular alternative to individual-level testing for disease screening. These reductions are obtained by testing pooled biospecimens (eg, blood, urine, swabs, etc.) for the presence of an infectious agent. However, these reductions come at the expense of data complexity, making the task of conducting disease surveillance more tenuous when compared to using individual-level data. This is because an individual's disease status may be obscured by a group testing protocol and the effect of imperfect testing. Furthermore, unlike individual-level testing, a given participant could be involved in multiple testing outcomes and/or may never be tested individually. To circumvent these complexities and to incorporate all available information, we propose a Bayesian generalized linear mixed model that accommodates data arising from any group testing protocol, estimates unknown assay accuracy probabilities and accounts for potential heterogeneity in the covariate effects across population subgroups (eg, clinic sites, etc.); this latter feature is of key interest to practitioners tasked with conducting disease surveillance. To achieve model selection, our proposal uses spike and slab priors for both fixed and random effects. The methodology is illustrated through numerical studies and is applied to chlamydia surveillance data collected in Iowa.

Rapid HIV Antigen-Antibody Assays and Detection of Acute HIV Infection in Sub-Saharan Africa

Detection of acute HIV infection is a unique problem that fourth-generation HIV assays were expected to alleviate. In this commentary, we draw attention to the limitations and challenges with use of currently available rapid antigen-antibody (Ag/Ab) combination tests for detection of acute HIV infection in sub-Saharan Africa. Laboratory-based HIV-1 Ag/Ab immunoassays are complex, requiring specialized equipment and handling that are currently not affordable in many settings in Africa. The point-of-care Ag/Ab platform on the other hand is easier to deploy and potentially more accessible in resource-limited settings. However, available fourth-generation HIV-1 rapid diagnostic tests have demonstrated poor performance characteristics in field studies where non-B subtypes of HIV-1 dominate. The potential for point-of-care HIV-1 Ag/Ab diagnostics to significantly improve detection of acute HIV infection remains yet to be realized in sub-Saharan Africa. Assay platforms need to be optimized to identify local circulating subtypes, and optimal algorithms need to be determined.

Adaptive elastic net for group testing

For disease screening, group (pooled) testing can be a cost-saving alternative to one-at-a-time testing, with savings realized through assaying pooled biospecimen (eg, urine, blood, saliva). In many group testing settings, practitioners are faced with the task of conducting disease surveillance. That is, it is often of interest to relate individuals' true disease statuses to covariate information via binary regression. Several authors have developed regression methods for group testing data, which is challenging due to the effects of imperfect testing. That is, all testing outcomes (on pools and individuals) are subject to misclassification, and individuals' true statuses are never observed. To further complicate matters, individuals may be involved in several testing outcomes. For analyzing such data, we provide a novel regression methodology which generalizes and extends the aforementioned regression techniques and which incorporates regularization. Specifically, for model fitting and variable selection, we propose an adaptive elastic net estimator under the logistic regression model which can be used to analyze data from any group testing strategy. We provide an efficient algorithm for computing the estimator along with guidance on tuning parameter selection. Moreover, we establish the asymptotic properties of the proposed estimator and show that it possesses "oracle" properties. We evaluate the performance of the estimator through Monte Carlo studies and illustrate the methodology on a chlamydia data set from the State Hygienic Laboratory in Iowa City.

Randomized Controlled Pilot Study of Antiretrovirals and a Behavioral Intervention for Persons With Acute HIV Infection: Opportunity for Interrupting Transmission

Background

Persons with acute HIV infection (AHI) have heightened transmission risk. We evaluated potential transmission reduction using behavioral and biomedical interventions in a randomized controlled pilot study in Malawi.

Methods

Persons were randomized 1:2:2 to standard counseling (SC), 5-session behavioral intervention (BI), or behavioral intervention plus 12 weeks of antiretrovirals (ARVs; BIA). All were followed for 26–52 weeks and, regardless of arm, referred for treatment according to Malawi-ARV guidelines. Participants were asked to refer partners for testing.

Results

Among 46 persons (9 SC, 18 BI, 19 BIA), the average age was 28; 61% were male. The median viral load (VL) was 5.9 log copies/mL at enrollment. 67% (10/15) of BIA participants were suppressed (<1000 copies/mL) at week 12 vs 25% BI and 50% SC (P = .07). Although the mean number of reported condomless sexual acts in the past week decreased from baseline across all arms (1.5 vs 0.3 acts), 36% experienced incident sexually transmitted infection by 52 weeks (12% SC, 28% BI, 18% BIA). Forty-one percent (19/46) of participants referred partners (44% SC, 44% BI, 37% BIA); 15 of the partners were HIV-infected.

Conclusions

Diagnosis of AHI facilitates behavioral and biomedical risk reduction strategies during a high-transmission period that begins years before people are typically identified and started on ARVs. Sexually transmitted infection incidence in this cohort suggests ongoing risk behaviors, reinforcing the importance of early intervention with ARVs to reduce transmission. Early diagnosis coupled with standard AHI counseling and early ARV referral quickly suppresses viremia, may effectively change behavior, and could have tremendous public health benefit in reducing onward transmission.

Group testing case identification with biomarker information

Screening procedures for infectious diseases, such as HIV, often involve pooling individual specimens together and testing the pools. For diseases with low prevalence, group testing (or pooled testing) can be used to classify individuals as diseased or not while providing considerable cost savings when compared to testing specimens individually. The pooling literature is replete with group testing case identification algorithms including Dorfman testing, higher-stage hierarchical procedures, and array testing. Although these algorithms are usually evaluated on the basis of the expected number of tests and classification accuracy, most evaluations in the literature do not account for the continuous nature of the testing responses and thus invoke potentially restrictive assumptions to characterize an algorithm's performance. Commonly used case identification algorithms in group testing are considered and are evaluated by taking a different approach. Instead of treating testing responses as binary random variables (i.e., diseased/not), evaluations are made by exploiting an assay's underlying continuous biomarker distributions for positive and negative individuals. In doing so, a general framework to describe the operating characteristics of group testing case identification algorithms is provided when these distributions are known. The methodology is illustrated using two HIV testing examples taken from the pooling literature.

Bayesian regression for group testing data

Group testing involves pooling individual specimens (e.g., blood, urine, swabs, etc.) and testing the pools for the presence of a disease. When individual covariate information is available (e.g., age, gender, number of sexual partners, etc.), a common goal is to relate an individual's true disease status to the covariates in a regression model. Estimating this relationship is a nonstandard problem in group testing because true individual statuses are not observed and all testing responses (on pools and on individuals) are subject to misclassification arising from assay error. Previous regression methods for group testing data can be inefficient because they are restricted to using only initial pool responses and/or they make potentially unrealistic assumptions regarding the assay accuracy probabilities. To overcome these limitations, we propose a general Bayesian regression framework for modeling group testing data. The novelty of our approach is that it can be easily implemented with data from any group testing protocol. Furthermore, our approach will simultaneously estimate assay accuracy probabilities (along with the covariate effects) and can even be applied in screening situations where multiple assays are used. We apply our methods to group testing data collected in Iowa as part of statewide screening efforts for chlamydia, and we make user-friendly R code available to practitioners.

Group testing regression models with dilution submodels

Group testing, where specimens are tested initially in pools, is widely used to screen individuals for sexually transmitted diseases. However, a common problem encountered in practice is that group testing can increase the number of false negative test results. This occurs primarily when positive individual specimens within a pool are diluted by negative ones, resulting in positive pools testing negatively. If the goal is to estimate a population-level regression model relating individual disease status to observed covariates, severe bias can result if an adjustment for dilution is not made. Recognizing this as a critical issue, recent binary regression approaches in group testing have utilized continuous biomarker information to acknowledge the effect of dilution. In this paper, we have the same overall goal but take a different approach. We augment existing group testing regression models (that assume no dilution) with a parametric dilution submodel for pool-level sensitivity and estimate all parameters using maximum likelihood. An advantage of our approach is that it does not rely on external biomarker test data, which may not be available in surveillance studies. Furthermore, unlike previous approaches, our framework allows one to formally test whether dilution is present based on the observed group testing data. We use simulation to illustrate the performance of our estimation and inference methods, and we apply these methods to 2 infectious disease data sets.

Improved matrix pooling

Pooled testing is useful to identify positive specimens for large-scale screening. Matrix pooling is one of the commonly used algorithms. In this work, we investigate the properties of matrix pooling and reveal that the efficiency of matrix pooling is related with the magnitude of overlapping among groups. Based on this property, we develop a new design to further improve the efficiency while taking into account of testing error. The efficiency, pooling sensitivity and specificity of this algorithm are explicitly derived and verified through plasmode simulation of detecting acute human immunodeficiency virus among patients who were suspected to have malaria in rural Ugandan. We show that the new design outperforms matrix pooling in efficiency while retain the pooling sensitivity and specificity.

Clinical and public health implications of acute and early HIV detection and treatment: a scoping review

INTRODUCTION

The unchanged global HIV incidence may be related to ignoring acute HIV infection (AHI). This scoping review examines diagnostic, clinical, and public health implications of identifying and treating persons with AHI.

METHODS

We searched PubMed, in addition to hand-review of key journals identifying research pertaining to AHI detection and treatment. We focused on the relative contribution of AHI to transmission and the diagnostic, clinical, and public health implications. We prioritized research from low- and middle-income countries (LMICs) published in the last fifteen years.

RESULTS AND DISCUSSION

Extensive AHI research and limited routine AHI detection and treatment have begun in LMIC. Diagnostic challenges include ease-of-use, suitability for application and distribution in LMIC, and throughput for high-volume testing. Risk score algorithms have been used in LMIC to screen for AHI among individuals with behavioural and clinical characteristics more often associated with AHI. However, algorithms have not been implemented outside research settings. From a clinical perspective, there are substantial immunological and virological benefits to identifying and treating persons with AHI - evading the irreversible damage to host immune systems and seeding of viral reservoirs that occurs during untreated acute infection. The therapeutic benefits require rapid initiation of antiretrovirals, a logistical challenge in the absence of point-of-care testing. From a public health perspective, AHI diagnosis and treatment is critical to: decrease transmission via viral load reduction and behavioural interventions; improve pre-exposure prophylaxis outcomes by avoiding treatment initiation for HIV-seronegative persons with AHI; and, enhance partner services via notification for persons recently exposed or likely transmitting.

CONCLUSIONS

There are undeniable clinical and public health benefits to AHI detection and treatment, but also substantial diagnostic and logistical barriers to implementation and scale-up. Effective early ART initiation may be critical for HIV eradication efforts, but widespread use in LMIC requires simple and accurate diagnostic tools. Implementation research is critical to facilitate sustainable integration of AHI detection and treatment into existing health systems and will be essential for prospective evaluation of testing algorithms, point-of-care diagnostics, and efficacious and effective first-line regimens.

Improving the Quality of Diagnostic Studies Evaluating Point of Care Tests for Acute HIV Infections: Problems and Recommendations

The diagnosis of acute human immunodeficiency virus (HIV) infection (AHI) plays a unique role in preventing the spread of HIV and ending the epidemic. Acutely infected individuals are thought to contribute substantially to forward transmissions of HIV; however, diagnosing AHI in resource-limited settings has proven to be a challenge. While fourth generation antigen-antibody combination assays have been successful in high-resource settings, rapid point of care (POC) versions of these assays have yet to demonstrate high sensitivity to detect AHI. Newer RNA/DNA based POC technologies are being validated, but the challenge to understand the additional value of these devices depends on the quality of study evaluations, in particular choice of study designs and case mix of included populations. In this commentary, we aimed to review the quality of studies evaluating a new fourth generation rapid test for detecting AHI, to identify general methodological limitations and biases in diagnostic accuracy studies, and to recommend strategies for avoiding them in future evaluations. The new studies that were evaluated continued to report the same weaknesses and biases that were seen in previous evaluations of fourth generation rapid tests. We recommend that investigators design future studies carefully, keeping in mind how diagnostic performance may be influenced by prevalence, population, patient case mixes, and reference standards. Care must be taken to avoid biases specific to diagnostic accuracy studies (spectrum, verification, incorporation and reference standard biases). To improve on quality, reporting checklists and guidelines such as Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Standards for Reporting Diagnostic accuracy studies (STARD) should be reviewed prior to conducting studies.

Estimating the prevalence of multiple diseases from two-stage hierarchical pooling

Testing protocols in large-scale sexually transmitted disease screening applications often involve pooling biospecimens (e.g., blood, urine, and swabs) to lower costs and to increase the number of individuals who can be tested. With the recent development of assays that detect multiple diseases, it is now common to test biospecimen pools for multiple infections simultaneously. Recent work has developed an expectation-maximization algorithm to estimate the prevalence of two infections using a two-stage, Dorfman-type testing algorithm motivated by current screening practices for chlamydia and gonorrhea in the USA. In this article, we have the same goal but instead take a more flexible Bayesian approach. Doing so allows us to incorporate information about assay uncertainty during the testing process, which involves testing both pools and individuals, and also to update information as individuals are tested. Overall, our approach provides reliable inference for disease probabilities and accurately estimates assay sensitivity and specificity even when little or no information is provided in the prior distributions. We illustrate the performance of our estimation methods using simulation and by applying them to chlamydia and gonorrhea data collected in Nebraska. Copyright © 2016 John Wiley & Sons, Ltd.

Incorporating Acute HIV Screening into Routine HIV Testing at Sexually Transmitted Infection Clinics, and HIV Testing and Counseling Centers in Lilongwe, Malawi

Background and Objectives:

Integrating acute HIV-infection (AHI) testing into clinical settings is critical to prevent transmission, and realize potential treatment-as-prevention benefits. We evaluated acceptability of AHI testing and compared AHI prevalence at sexually transmitted infection (STI) clinics and HIV testing and counseling (HTC) clinics in Lilongwe, Malawi.

Methods:

We conducted HIV RNA testing for HIV-seronegative patients visiting STI and HTC clinics. AHI was defined as positive RNA and negative/discordant rapid antibody tests. We evaluated demographic, behavioral, and transmission-risk differences between STI and HTC patients and assessed performance of a risk-score for targeted screening.

Results:

Nearly two-thirds (62.8%, 9280/14,755) of eligible patients consented to AHI testing. We identified 59 persons with AHI (prevalence = 0.64%)–a 0.9% case-identification increase. Prevalence was higher at STI [1.03% (44/4255)] than at HTC clinics [0.3% (15/5025), P < 0.01], accounting for 2.3% of new diagnoses vs 0.3% at HTC clinic. Median viral load (VL) was 758,050 copies per milliliter; 25% (15/59) had VL ≥10,000,000 copies per milliliter. Median VL was higher at STI (1,000,000 copies/mL) compared with HTC (153,125 copies/mL, P = 0.2). Among persons with AHI, those tested at STI clinics were more likely to report genital sores compared with those tested at HTC clinics (54.6% vs 6.7%, P < 0.01). The risk score algorithm performed well in identifying persons with AHI at HTC clinics (sensitivity = 73%, specificity = 89%).

Conclusions:

The majority of patients consented to AHI testing. AHI prevalence was substantially higher in STI clinics than HTC clinics. Remarkably high VLs and concomitant genital scores demonstrate the potential for transmission. Universal AHI screening at STI clinics, and targeted screening at HTC centers, should be considered.

High rate of missed HIV infections in individuals with indeterminate or negative HIV western blots based on current HIV testing algorithm in China

It remains unclear if China's current HIV antibody testing algorithm misses a substantial number of HIV infected individuals. Of 196 specimens with indeterminate or negative results on HIV western blot (WB) retrospectively examined by HIV-1 nucleic acid test (NAT), 67.57% (75/111) of indeterminate WB samples, and 16.47% (14/85) of negative WB samples were identified as NAT positive. HIV-1 loads in negative WB samples were significantly higher than those in indeterminate WB samples. Notably, 86.67% (13/15) of samples with negative WB and double positive immunoassay results were NAT positive. The rate of HIV-1 infections missed by China's current HIV testing algorithm is unacceptably high. Thus, China should consider using NAT or integrating fourth generation ELISA into current only antibodies-based HIV confirmation. J. Med. Virol. 88:1462-1466, 2016. © 2016 Wiley Periodicals, Inc.

The HIV treatment cascade in acutely infected people: informing global guidelines

PURPOSE OF REVIEW

Acute and early HIV (AHI) is a pivotal time during HIV infection, yet there remain major shortfalls in diagnosis, linkage to care, and antiretroviral therapy (ART) initiation during AHI. We introduce an AHI-specific cascade, review recent evidence pertaining to the unique challenges of AHI, and discuss strategies for improving individual and public health outcomes.

RECENT FINDINGS

Presentation during AHI is common. Expanding use of fourth-generation testing and pooled nucleic acid amplification testing has led to improved AHI detection in resource-wealthy settings. Technologies capable of AHI diagnosis are rare in resource-limited settings; further development of point-of-care devices and utilization of targeted screening is needed. Rapid ART initiation during AHI limits reservoir seeding, preserves immunity, and prevents transmission. Reporting of AHI cascade outcomes is limited, but new evidence suggests that impressive rates of diagnosis, linkage to care, rapid ART initiation, and viral suppression can be achieved.

SUMMARY

With advancements in AHI diagnostics and strong evidence for the therapeutic and prevention benefits of ART initiated during AHI, improving AHI cascade outcomes is both crucial and feasible. HIV guidelines should recommend diagnostic algorithms capable of detecting AHI and prescribe rapid, universal ART initiation during AHI.

Costs per Diagnosis of Acute HIV Infection in Community-based Screening Strategies: A Comparative Analysis of Four Screening Algorithms

BACKGROUND

In nonhealthcare settings, widespread screening for acute human immunodeficiency virus (HIV) infection (AHI) is limited by cost and decision algorithms to better prioritize use of resources. Comparative cost analyses for available strategies are lacking.

METHODS

To determine cost-effectiveness of community-based testing strategies, we evaluated annual costs of 3 algorithms that detect AHI based on HIV nucleic acid amplification testing (EarlyTest algorithm) or on HIV p24 antigen (Ag) detection via Architect (Architect algorithm) or Determine (Determine algorithm) as well as 1 algorithm that relies on HIV antibody testing alone (Antibody algorithm). The cost model used data on men who have sex with men (MSM) undergoing community-based AHI screening in San Diego, California. Incremental cost-effectiveness ratios (ICERs) per diagnosis of AHI were calculated for programs with HIV prevalence rates between 0.1% and 2.9%.

RESULTS

Among MSM in San Diego, EarlyTest was cost-savings (ie, ICERs per AHI diagnosis less than $13.000) when compared with the 3 other algorithms. Cost analyses relative to regional HIV prevalence showed that EarlyTest was cost-effective (ie, ICERs less than $69.547) for similar populations of MSM with an HIV prevalence rate >0.4%; Architect was the second best alternative for HIV prevalence rates >0.6%.

CONCLUSIONS

Identification of AHI by the dual EarlyTest screening algorithm is likely to be cost-effective not only among at-risk MSM in San Diego but also among similar populations of MSM with HIV prevalence rates >0.4%.

Implementing early diagnosis and treatment: programmatic considerations

PURPOSE OF REVIEW

To benefit very early treatment for an HIV cure, HIV infection must be diagnosed immediately after infection. We review potential strategies for expansion of HIV testing in preparation for a potential HIV cure in the distant future.

RECENT FINDINGS

Very early antiretroviral therapy (ART) initiated during acute or early HIV infection may result in long-term viral control. Such strategies will require programmatic systems for very early diagnosis and very early ART. Yet, most HIV-infected individuals start treatment late and presumably are diagnosed late. Operational issues will have to address the treatment cascade much earlier starting from HIV testing, test result notification, posttest counseling, immunological and clinical assessment and ultimately starting treatment. Well designed implementation research studies are needed to determine effective interventions to diagnose HIV as early as during acute and early HIV infection.

SUMMARY

Approaches for earlier HIV diagnosis and very early ART for a potential functional HIV cure remain elusive unless early HIV diagnosis can be radically expanded.