Integration of Contact Tracing and Phylogenetics in an Investigation of Acute HIV Infection

HIV transmission among acutely infected participants of a Dutch cohort study 2015-2021 is not associated with large, clustered outbreaks

OBJECTIVE

Timely identification of acute or early HIV infection (AEHI) is important to help prevent onward transmission, and understanding the number of secondary infections resulting from individuals with AEHI is key to planning HIV prevention services and case finding.

DESIGN

We performed a phylogenetic investigation of a dense sample of individuals with AEHI who took part in the Netherlands Cohort Study on Acute HIV infection (NOVA) in the Netherlands during 2015-2021.

METHODS

Transmission clusters were identified using phylogenetic analyses based on HIV pol sequences. The Tamura-Nei model was used to estimate genetic distance. A number of 1000 bootstraps was used to check the reliability of clustering using maximum likelihood. A cluster was defined as having a bootstrap value of at least 95% and a genetic distance of at most 1.5%. Sensitivity analyses using different values for the bootstrap and genetic distance were performed to study the reproducibility of the clustering.

RESULTS

Of the 156 participants included in NOVA between July 2015 and April 2021, 134 individuals for whom baseline characteristics and genotypic resistance data at baseline were available could be included. We identified 10 clusters, but the majority of persons (111/134) were not part of a cluster, suggesting mainly independent transmission events.

CONCLUSION

Mainly independent transmission events among a study population consisting predominantly of MSM in a low-incidence high-resource setting is likely the result of active AEHI case finding and direct start of treatment, and the roll-out over recent years of preventive measures such as preexposure prophylaxis.

Impact of molecular sequence data completeness on HIV cluster detection and a network science approach to enhance detection

Detection of viral transmission clusters using molecular epidemiology is critical to the response pillar of the Ending the HIV Epidemic initiative. Here, we studied whether inference with an incomplete dataset would influence the accuracy of the reconstructed molecular transmission network. We analyzed viral sequence data available from ~ 13,000 individuals with diagnosed HIV (2012-2019) from Houston Health Department surveillance data with 53% completeness (n = 6852 individuals with sequences). We extracted random subsamples and compared the resulting reconstructed networks versus the full-size network. Increasing simulated completeness was associated with an increase in the number of detected clusters. We also subsampled based on the network node influence in the transmission of the virus where we measured Expected Force (ExF) for each node in the network. We simulated the removal of nodes with the highest and then lowest ExF from the full dataset and discovered that 4.7% and 60% of priority clusters were detected respectively. These results highlight the non-uniform impact of capturing high influence nodes in identifying transmission clusters. Although increasing sequence reporting completeness is the way to fully detect HIV transmission patterns, reaching high completeness has remained challenging in the real world. Hence, we suggest taking a network science approach to enhance performance of molecular cluster detection, augmented by node influence information.

HIV Response Interventions that Integrate HIV Molecular Cluster and Social Network Analysis: A Systematic Review

Due to improved efficiency and reduced cost of viral sequencing, molecular cluster analysis can be feasibly utilized alongside existing human immunodeficiency virus (HIV) prevention strategies. The goal of this paper is to elucidate how HIV molecular cluster and social network analyses are being integrated to implement HIV response interventions. We searched PubMed, Scopus, PsycINFO, and Cochrane Library databases for studies incorporating both HIV molecular cluster and social network data. We identified 32 articles that combined analyses of HIV molecular sequences and social or sexual networks. All studies were descriptive. Six studies described network interventions informed by molecular and social data but did not fully evaluate their efficacy. There is no current standard for incorporating molecular and social network analyses to inform interventions or data demonstrating its utility. More research must be conducted to delineate benefits and best practices for leveraging molecular data for network-based interventions.

Use of Injection Drugs and Any Form of Methamphetamine in the Portland, OR Metro Area as a Driver of an HIV Time-Space Cluster: Clackamas, Multnomah, and Washington Counties, 2018-2020

We describe the response to detection of a time-space cluster of new HIV infection in the Portland, OR metro area among people who inject drugs (PWID) and/or people who use any form of methamphetamine. This time-space cluster took place in a region with a syndemic of homelessness and drug use. The investigation included new HIV diagnoses in 2018, 2019, and 2020 in Clackamas, Multnomah, and Washington Counties. Public health response included activating incident command, development and implementation of an enhanced interview tool, outreach testing, and stakeholder engagement. We identified 396 new cases of HIV infection, 116 (29%) of which met the cluster definition. Most cluster cases had no molecular relationships to previous cases. Persons responding to the enhanced interview tool reported behaviors associated with HIV acquisition. Field outreach testing did not identify any new HIV cases but did identify hepatitis C and syphilis infections. We show the importance of a robust public health response to a time-space cluster of new HIV infections in an urban area.

HIV Cluster and Outbreak Detection and Response: The Science and Experience

The Respond pillar of the Ending the HIV Epidemic in the U.S. initiative, which consists of activities also known as cluster and outbreak detection and response, offers a framework to guide tailored implementation of proven HIV prevention strategies where transmission is occurring most rapidly. Cluster and outbreak response involves understanding the networks in which rapid transmission is occurring; linking people in the network to essential services; and identifying and addressing gaps in programs and services such as testing, HIV and other medical care, pre-exposure prophylaxis, and syringe services programs. This article reviews the experience gained through 30 HIV cluster and outbreak responses in North America during 2000-2020 to describe approaches for implementing these core response strategies. Numerous jurisdictions that have implemented these response strategies have demonstrated success in improving outcomes related to HIV care and viral suppression, testing, use of prevention services, and reductions in transmission or new diagnoses. Efforts to address important gaps in service delivery revealed by cluster and outbreak detection and response can strengthen prevention efforts broadly through multidisciplinary, multisector collaboration. In this way, the Respond pillar embodies the collaborative, data-guided approach that is critical to the overall success of the Ending the HIV Epidemic in the U.S. initiative.

Sorting by Race/Ethnicity Across HIV Genetic Transmission Networks in Three Major Metropolitan Areas in the United States

An important component underlying the disparity in HIV risk between race/ethnic groups is the preferential transmission between individuals in the same group. We sought to quantify transmission between different race/ethnicity groups and measure racial assortativity in HIV transmission networks in major metropolitan areas in the United States. We reconstructed HIV molecular transmission networks from viral sequences collected as part of HIV surveillance in New York City, Los Angeles County, and Cook County, Illinois. We calculated assortativity (the tendency for individuals to link to others with similar characteristics) across the network for three candidate characteristics: transmission risk, age at diagnosis, and race/ethnicity. We then compared assortativity between race/ethnicity groups. Finally, for each race/ethnicity pair, we performed network permutations to test whether the number of links observed differed from that expected if individuals were sorting at random. Transmission networks in all three jurisdictions were more assortative by race/ethnicity than by transmission risk or age at diagnosis. Despite the different race/ethnicity proportions in each metropolitan area and lower proportions of clustering among African Americans than other race/ethnicities, African Americans were the group most likely to have transmission partners of the same race/ethnicity. This high level of assortativity should be considered in the design of HIV intervention and prevention strategies.

HIV-1 Transmission linkages among persons with incident infection to inform public health surveillance

BACKGROUND

We evaluated features of HIV transmission networks involving persons diagnosed during incident HIV infection (IHI) to assess network-based opportunities to curtail onward transmission.

METHODS

Transmission networks were constructed using partial pol sequences reported to North Carolina surveillance among persons with recent (2014-2018) and past (<2014) HIV diagnoses. IHI were defined as documented acute infections or seroconversion. Demographic and virologic features of HIV genetic clusters (<1.5% pairwise genetic distance) involving ≥ 1 IHI were assessed. Persons with viral genetic links and who had diagnoses >90 days prior to an IHI were further characterized. We assessed named partner outcomes among IHI index persons using contact tracing data.

FINDINGS

Of 4,405 HIV diagnoses 2014-2018 with sequences, there were 323 (7%) IHI index persons; most were male (88%), Black (65%), young (68% <30 years), and reported sex with men (MSM) risk (79%). Index persons were more likely to be cluster members compared to non-index persons diagnosed during the same period (72% vs. 49%). In total, 162 clusters were identified involving 233 IHI, 577 recent diagnoses, and 163 past diagnoses. Most IHI cases (53%) had viral linkages to ≥1 previously diagnosed person without evidence of HIV viral suppression in the year prior to the diagnosis of the IHI index. In contact tracing, only 53% IHI cases named an HIV-positive contact, resulting in 0.5 previously diagnosed persons detected per IHI investigated. When combined with viral analyses, the detection rate of viremic previously diagnosed persons increased to 1.3.

INTERPRETATION

Integrating public health with molecular epidemiology, revealed that more than half of IHI have viral links to persons with previously diagnosed unsuppressed HIV infection which was largely unrecognized by traditional contact tracing. Enhanced partner services to support engagement and retention in HIV care and improved case finding supported by rapid phylogenetic analysis are tools to substantially reduce onward HIV transmission.

Peer Mobilization and Human Immunodeficiency Virus (HIV) Partner Notification Services Among Gay, Bisexual, and Other Men Who Have Sex With Men and Transgender Women in Coastal Kenya Identified a High Number of Undiagnosed HIV Infections

Background

Human immunodeficiency virus (HIV) partner notification services (HPN), peer mobilization with HIV self-testing, and acute and early HIV infection (AEHI) screening among gay, bisexual, and other men who have sex with men (GBMSM) and transgender women (TGW) were assessed for acceptability, feasibility, and linkage to antiretroviral therapy (ART) and preexposure prophylaxis (PrEP) services.

Methods

Between April and August 2019, peer mobilizers mobilized clients by offering HIV oral self-tests and immediate clinic referral for clients with AEHI symptoms. Mobilized participants received clinic-based rapid antibody testing and point-of-care HIV RNA testing. Newly diagnosed participants including those derived from HIV testing services were offered immediate ART and HPN. Partners were recruited through HPN.

Results

Of 772 mobilized clients, 452 (58.5%) enrolled in the study as mobilized participants. Of these, 16 (3.5%) were HIV newly diagnosed, including 2 (0.4%) with AEHI. All but 2 (14/16 [87.5%]) initiated ART. Thirty-five GBMSM and TGW were offered HPN and 27 (77.1%) accepted it. Provider referral identified a higher proportion of partners tested (39/64 [60.9%] vs 5/14 [35.7%]) and partners with HIV (27/39 [69.2%] vs 2/5 [40.0%]) than index referral. Of 44 enrolled partners, 10 (22.7%) were newly diagnosed, including 3 (6.8%) with AEHI. All 10 (100%) initiated ART. PrEP was initiated among 24.0% (103/429) mobilized participants and 28.6% (4/14) partners without HIV.

Conclusions

HPN, combined with a peer mobilization-led self-testing strategy and AEHI screening for GBMSM and TGW, appears to be acceptable and feasible. These strategies, especially HPN provider referral, effectively identified undiagnosed HIV infections and linked individuals to ART and PrEP services.

Factors Associated With Human Immunodeficiency Virus Infections Linked in Genetic Clusters But Disconnected in Partner Tracing

BACKGROUND

Successful partner notification can improve community-level outcomes by increasing the proportion of persons living with human immunodeficiency virus (HIV) who are linked to HIV care and virally suppressed, but it is resource intensive. Understanding where HIV transmission pathways may be undetected by routine partner notification may help improve case finding strategies.

METHODS

We combined partner notification interview and HIV sequence data for persons diagnosed with HIV in Wake County, NC in 2012 to 2013 to evaluate partner contact networks among persons with HIV pol gene sequences 2% or less pairwise genetic distance. We applied a set of multivariable generalized estimating equations to identify correlates of disparate membership in genetic versus partner contact networks.

RESULTS

In the multivariable model, being in a male-male pair (adjusted odds ratio [AOR], 16.7; P = 0.01), chronic HIV infection status (AOR, 4.5; P < 0.01), and increasing percent genetic distance between each dyad member's HIV pol gene sequence (AOR, 8.3 per each 1% increase, P < 0.01) were all associated with persons with HIV clustering but not being identified in the partner notification network component. Having anonymous partners or other factors typically associated with risk behavior were not associated.

CONCLUSIONS

Based on genetic networks, partnerships which may be stigmatized, may have occurred farther back in time or may have an intervening partner were more likely to be unobserved in the partner contact network. The HIV genetic cluster information contributes to public health understanding of HIV transmission networks in these settings where partner identifying information is not available.

Human Immunodeficiency Virus (HIV)-1 Transmission Among Persons With Acute HIV-1 Infection in Malawi: Demographic, Behavioral, and Phylogenetic Relationships

BACKGROUND

Understanding sexual networks involving acute human immunodeficiency virus (HIV)-1 infections (AHI) may lead to prevention opportunities to mitigate high rates of onward transmission. We evaluated HIV-1 phylogenetic and behavioral characteristics among persons with AHI and their referred partners.

METHODS

Between 2012 and 2014, 46 persons with AHI in Malawi participated in a combined behavioral and biomedical intervention. Participants referred sexual partners by passive referral. Demographics and sexual behaviors were collected through interviews and HIV-1 genetic relationships were assessed with phylogenetics.

RESULTS

Among 45 AHI participants with HIV-1 sequences, none was phylogenetically-linked with another AHI index. There were 19 (42%) AHI participants who referred a single partner that returned for testing. Most partners (n = 17) were HIV-infected, with 15 (88%) presenting with an established infection. There were 14 index-partner pairs that had sequences available; 13 (93%) pairs were phylogenetically-linked dyads. The AHI index was female in 7/13 (54%) dyads. Age-disparate relationships among dyads were common (≥5-year age difference in 67% of dyads), including 3/6 dyads involving a male index and a younger woman. Index participants with a referred partner were more likely to report no casual partners and to be living with their current partner than participants not in dyads.

CONCLUSIONS

Passive-partner referral successfully identified partners with genetically-similar HIV infections-the likely source of infection-but only 40% of index cases referred partners who presented for HIV-1 testing. Future work evaluating assisted partner notification may help reach susceptible partners or more people with untreated HIV-1 infections connected to acute transmission.

CLINICAL TRIALS REGISTRATION

NCT01450189.

Molecular network-based intervention brings us closer to ending the HIV pandemic

Precise identification of HIV transmission among populations is a key step in public health responses. However, the HIV transmission network is usually difficult to determine. HIV molecular networks can be determined by phylogenetic approach, genetic distance-based approach, and a combination of both approaches. These approaches are increasingly used to identify transmission networks among populations, reconstruct the history of HIV spread, monitor the dynamics of HIV transmission, guide targeted intervention on key subpopulations, and assess the effects of interventions. Simulation and retrospective studies have demonstrated that these molecular network-based interventions are more cost-effective than random or traditional interventions. However, we still need to address several challenges to improve the practice of molecular network-guided targeting interventions to finally end the HIV epidemic. The data remain limited or difficult to obtain, and more automatic real-time tools are required. In addition, molecular and social networks must be combined, and technical parameters and ethnic issues warrant further studies.

Phylodynamic Analysis Complements Partner Services by Identifying Acute and Unreported HIV Transmission

Tailoring public health responses to growing HIV transmission clusters depends on accurately mapping the risk network through which it spreads and identifying acute infections that represent the leading edge of cluster growth. HIV transmission links, especially those involving persons with acute HIV infection (AHI), can be difficult to uncover, or confirm during partner services investigations. We integrated molecular, epidemiologic, serologic and behavioral data to infer and evaluate transmission linkages between participants of a prospective study of AHI conducted in North Carolina, New York City and San Francisco from 2011-2013. Among the 547 participants with newly diagnosed HIV with polymerase sequences, 465 sex partners were reported, of whom only 35 (7.5%) had HIV sequences. Among these 35 contacts, 23 (65.7%) links were genetically supported and 12 (34.3%) were not. Only five links were reported between participants with AHI but none were genetically supported. In contrast, phylodynamic inference identified 102 unreported transmission links, including 12 between persons with AHI. Importantly, all putative transmission links between persons with AHI were found among large clusters with more than five members. Taken together, the presence of putative links between acute participants who did not name each other as contacts that are found only among large clusters underscores the potential for unobserved or undiagnosed intermediaries. Phylodynamics identified many more links than partner services alone and, if routinely and rapidly integrated, can illuminate transmission patterns not readily captured by partner services investigations.

Prediction of HIV Transmission Cluster Growth With Statewide Surveillance Data

BACKGROUND

Prediction of HIV transmission cluster growth may help guide public health action. We developed a predictive model for cluster growth in North Carolina (NC) using routine HIV surveillance data.

METHODS

We identified putative transmission clusters with ≥2 members through pairwise genetic distances ≤1.5% from HIV-1 pol sequences sampled November 2010-December 2017 in NC. Clusters established by a baseline of January 2015 with any sequences sampled within 2 years before baseline were assessed for growth (new diagnoses) over 18 months. We developed a predictive model for cluster growth incorporating demographic, clinical, temporal, and contact tracing characteristics of baseline cluster members. We internally and temporally externally validated the final model in the periods January 2015-June 2016 and July 2016-December 2017.

RESULTS

Cluster growth was predicted by larger baseline cluster size, shorter time between diagnosis and HIV care entry, younger age, shorter time since the most recent HIV diagnosis, higher proportion with no named contacts, and higher proportion with HIV viremia. The model showed areas under the receiver-operating characteristic curves of 0.82 and 0.83 in the internal and temporal external validation samples.

CONCLUSIONS

The predictive model developed and validated here is a novel means of identifying HIV transmission clusters that may benefit from targeted HIV control resources.

Leveraging Phylogenetics to Understand HIV Transmission and Partner Notification Networks

BACKGROUND

Partner notification is an important component of public health test and treat interventions. To enhance this essential function, we assessed the potential for molecular methods to supplement routine partner notification and corroborate HIV networks.

METHODS

All persons diagnosed with HIV infection in Wake County, NC, during 2012-2013 and their disclosed sexual partners were included in a sexual network. A data set containing HIV-1 pol sequences collected in NC during 1997-2014 from 15,246 persons was matched to HIV-positive persons in the network and used to identify putative transmission clusters. Both networks were compared.

RESULTS

The partner notification network comprised 280 index cases and 383 sexual partners and high-risk social contacts (n = 131 HIV-positive). Of the 411 HIV-positive persons in the partner notification network, 181 (44%) did not match to a HIV sequence, 61 (15%) had sequences but were not identified in a transmission cluster, and 169 (41%) were identified in a transmission cluster. More than half (59%) of transmission clusters bridged sexual network partnerships that were not recognized in the partner notification; most of these clusters were dominated by men who have sex with men.

CONCLUSIONS

Partner notification and HIV sequence analysis provide complementary representations of the existent partnerships underlying the HIV transmission network. The partner notification network components were bridged by transmission clusters, particularly among components dominated by men who have sex with men. Supplementing the partner notification network with phylogenetic data highlighted avenues for intervention.

Identifying Clusters of Recent and Rapid HIV Transmission Through Analysis of Molecular Surveillance Data

BACKGROUND

Detecting recent and rapid spread of HIV can help prioritize prevention and early treatment for those at highest risk of transmission. HIV genetic sequence data can identify transmission clusters, but previous approaches have not distinguished clusters of recent, rapid transmission. We assessed an analytic approach to identify such clusters in the United States.

METHODS

We analyzed 156,553 partial HIV-1 polymerase sequences reported to the National HIV Surveillance System and inferred transmission clusters using 2 genetic distance thresholds (0.5% and 1.5%) and 2 periods for diagnoses (all years and 2013-2015, ie, recent diagnoses). For rapidly growing clusters (with ≥5 diagnoses during 2015), molecular clock phylogenetic analysis estimated the time to most recent common ancestor for all divergence events within the cluster. Cluster transmission rates were estimated using these phylogenies.

RESULTS

A distance threshold of 1.5% identified 103 rapidly growing clusters using all diagnoses and 73 using recent diagnoses; at 0.5%, 15 clusters were identified using all diagnoses and 13 using recent diagnoses. Molecular clock analysis estimated that the 13 clusters identified at 0.5% using recent diagnoses had been diversifying for a median of 4.7 years, compared with 6.5-13.2 years using other approaches. The 13 clusters at 0.5% had a transmission rate of 33/100 person-years, compared with previous national estimates of 4/100 person-years.

CONCLUSIONS

Our approach identified clusters with transmission rates 8 times those of previous national estimates. This method can identify groups involved in rapid transmission and help programs effectively direct and prioritize limited public health resources.

Estimating Effects of HIV Sequencing Data Completeness on Transmission Network Patterns and Detection of Growing HIV Transmission Clusters

HIV nucleotide sequence data can identify clusters of persons with genetically similar strains suggesting transmission. We simulated the effect of lowered data completeness, defined by the percent of persons with diagnosed HIV with a reported sequence, on transmission patterns and detection of growing HIV transmission clusters. We analyzed HIV surveillance data for persons with HIV diagnosed during 2008-2014 who resided in Michigan or Washington. We calculated genetic distances, constructed the inferred transmission network for each jurisdiction, and compared transmission network characteristics and detection of growing transmission clusters in the full dataset with artificially reduced datasets. Simulating lower levels of completeness resulted in decreased percentages of persons linked to a cluster from high completeness (full dataset) to low completeness (5%) (Michigan: 54%-18%; Washington, 46%-16%). Patterns of transmission between certain populations remained robust as data completeness level was reduced. As data completeness was artificially decreased, sensitivity of cluster detection substantially diminished in both states. In Michigan, sensitivity decreased from 100% with the full dataset, to 62% at 50% completeness and 21% at 25% completeness. In Washington, sensitivity decreased from 100% with the full dataset, to 71% at 50% completeness and 29% at 25% completeness. Lower sequence data completeness limits the ability to detect clusters that may benefit from investigation; however, inferences can be made about transmission patterns even with low data completeness, given sufficient numbers. Data completeness should be prioritized, as lack of or delays in detection of transmission clusters could result in additional infections.