Interrupting HIV Transmission Networks: How can we design and implement timely and effective interventions?

1. Introduction

While remarkable progress has been achieved in HIV treatment and prevention over the past decade through widespread access to and use of antiretrovirals, HIV incidence has yet to substantially decline among key population in the United States (US). The federal Ending the HIV Epidemic (EHE) initiative launched in 2019 aims to reduce HIV infections by 90% by 2030 through focusing on four pillars: rapid diagnosis, early antiretroviral treatment (ART) initiation, expanded prevention (pre-exposure prophylaxis [PrEP]), and rapid response to outbreaks [1]. Mathematical modeling of the implementation of the first three pillars suggests that while substantial reductions in HIV incidence can be achieved, reaching EHE targets may be difficult in many jurisdictions [2]. There is no “one-size fits all” combination of strategies because of the heterogeneity of the epidemic across population groups and geography.

Planning interventions directed towards ongoing transmission thus requires a detailed understanding of the local epidemiology. The US HIV epidemic is comprised of sub-epidemics that overlap in time and space, formed by a confluence of biological as well as social factors. Ultimately, the intersection of sexual and/or injection drug use (IDU) behaviors in networks with HIV viremia leads to ongoing HIV transmission. Overall, nearly 36% of PWH in the US are not virally suppressed, and there are significant disparities along the HIV care continuum that persist by geographic region, between racial/ethnic groups, and sexual gender minority groups [3]. The entry of HIV into IDU networks, which have increased over the past decade due to the surge in the US opioid epidemic, has led to several explosive HIV outbreaks [4]. Consequently, several monitoring systems are employed to detect geographic areas with rapid HIV transmissions [5,6]. In many areas new HIV infections may not arise from explosive cluster outbreaks [7] yet should remain important in public health monitoring and response activities.

We outline here several elements that are key when designing effective and timely interventions based on HIV information reported into public health data systems including through the lens of current practices using HIV sequence data. While adequate public health funding and capacity building are paramount to successful interventions, we concentrate here on the importance of detecting early “incident” HIV infection, understanding local HIV transmission networks and outbreak detection, and the need for meaningful community engagement.

2. Importance of acute and early HIV infection detection for public health

As EHE targets are reached, the ability to detect and respond to incident HIV infection will become increasingly important in “getting to zero” new infections. Acute and early infections are sentinel events and represent the leading edge of the epidemic. Early infection is variably defined as the weeks to months following HIV acquisition, characterized by a period of high viremia that stabilizes to lower levels and antibodies become detectable [8]. Because clinical manifestations, if present, are often non-specific febrile syndromes, most individuals do not receive HIV testing and thus are unaware of their infection. Coupled with the high viremia in early infection, there is an estimated two-fold higher transmission rate during early infection compared to chronic (established) infection [9]. Such transmission rates are used as parameters for modeling studies to estimate the proportion of transmissions that are attributable across the different stages of the care continuum [9]. In the US, persons unaware of their infection are estimated to represent 14.9% of PWH, but account for 37.6% of HIV transmission while 33.9% of PWH are aware but not suppressed contribute to 62.4% of transmissions [9]. Gaps in the EHE pillars can be identified through careful understanding of the sources and rates of HIV transmission within a community. Similarly, failure to detect incident HIV can hinder the success of interventions.

3. What can we learn from molecular HIV epidemiology (MHE)?

Assessing the drivers of local HIV transmission and distinguishing between imported infection versus local transmission networks using MHE can help ensure the appropriate allocation of public health resources. MHE can be broadly defined as the study of HIV transmission dynamics through linkage between HIV nucleotide sequences to infer putative transmission networks. These putative networks, or genetic clusters, are either identified by phylogenetic analyses and inference of common ancestors or by comparison of pairwise genetic or patristic distances [10]. HIV nucleotide sequences have increased in number due to routine antiretroviral drug resistance testing sent during clinical care to test for transmitted or acquired drug resistance. Subsequently, these sequences have been used in MHE research studies, and since 2018, have been reported to the National HIV Surveillance System (NHSS).

There are variable definitions used to define clusters and the selection of cutoffs depends on the analytic objectives [10]. Short genetic distances between sequences indicate less evolutionary changes therefore less time elapsed since transmission and sampling. Short genetic distance cutoffs (0.5%) to define clusters increases the chance the sequences are related by recent and rapid transmission – an estimated 2–3 years [5]. However in a phylogenetic analyses among transmission pairs, high statistical support of the ancestral node defining the cluster favored recent transmission rather than genetic distance [11]. While higher genetic distance cutoffs for clusters are more likely to identify old transmission events, these may be appropriate for determining all possible related transmissions [10] and monitoring transmission over longer time periods and/or across geography. Genetic cluster analyses, whether from phylogenetics or pairwise genetic distances, cannot determine transmission directionality or identify individuals disproportionately contributing to transmission (i.e., “superspreaders”). Several additional limitations of MHE should be noted, including as expected that only PWH who have sequences reported will be represented. Other members of the transmission network will be missed such as PWH who are undiagnosed or sexual or needle sharing partners at risk for HIV acquisition. Importantly, very low sampling of the population (<10% with sequences) can lead to biased (spurious) clustering [10].

Outbreak detection and response

The CDC’s Cluster Detection and Response (CDR) program is currently focused towards clusters identified by recent and rapid HIV transmission [5] and/or time-space analyses [12]. With CDC guidance many local jurisdictions routinely analyze their public health data, develop response plans, and tailor local efforts as needed. Early detection of HIV outbreaks followed by prompt mitigation have been successful in containing several IDU-related epidemics [4] but are resource intensive due to the short lead time for outbreak detection and response. The CDR program prioritizes molecular clusters that include at least 3–5 PWH diagnosed in the previous 12 months with HIV sequences separated by <0.5% genetic distance. With stringent thresholds, only a minority of newly reported HIV diagnoses are identified in these clusters. Among the largest clusters identified from 2019–2020, most involved men who have sex with men and were small at initial detection and noted to grow rapidly [13]. These clusters varied demographically, again calling for tailored rather than generalized response interventions [13].

Response to Incident Infections

The Respond Pillar of the EHE initiative should not lose sight of the importance of monitoring and response to incident infections in addition to large genetic clusters. There are no evidence-based metrics yet available showing the effectiveness of cluster prioritization and response; most supportive data to date derives from outbreak responses. To be successful, response activities towards clusters need to focus on those clusters that are actively growing or predicted to grow, and current responses do not independently consider non-clustered acute or recent incident infections. A modeling study in San Diego found that incident HIV infection was a major driver of cluster growth, and that stringent CDC thresholds may fail to identify linked incident infections in similar concentrated epidemics [14]. Further investigation is needed to optimize cluster prioritization and the use MHE and contact tracing linkages to understand sources of incident infection [7].

4. The importance of meaningful community engagement in deploying public health resources

Leveraging community-based organizations (CBOs) and local advocacy groups is essential for the success of public health initiatives. Interventions based on HIV related public health data in particular face challenges such as ongoing societal stigma, misinformation about HIV treatment and prevention, and health system mistrust stemming from historical marginalization among racial minority groups who now experience the highest HIV burden. While name-based reporting and confidential partner notification of new HIV diagnoses has been routine in most states since the early days of the epidemic, the volume of clinical data collected and used for response has expanded. Many PWH and other community members may be unfamiliar with such reporting and those public health programs that use data to help prioritize services for PWH who appear to be out of care. The required reporting of sequences to the NHSS and subsequent routine CDR activities have fueled ongoing debates on the ethical implications on the use of such data by the public health systems [15]. Assurances in public health data privacy, security, and confidentiality remain paramount, including how such public health data can be protected from use in control measure violations. Regardless of the extent MHE is used in response activities, ongoing collaborative partnerships with CBOs and other community groups will foster trust and accountability that are essential elements of successful interventions.