Simulation of Full HIV Cluster Networks in a Nationally Representative Model Indicates Intervention Opportunities.

BACKGROUND: Clusters of rapid HIV transmission in the United States are increasingly recognized through analysis of HIV molecular sequence data reported to the National HIV Surveillance System. Understanding the full extent of cluster networks is important to assess intervention opportunities. However, full cluster networks include undiagnosed and other infections that cannot be systematically observed in real life. METHODS: We replicated HIV molecular cluster networks during 2015-2017 in the United States using a stochastic dynamic network simulation model of sexual transmission of HIV. Clusters were defined at the 0.5% genetic distance threshold. Ongoing priority clusters had growth of ≥3 diagnoses/year in multiple years; new priority clusters first had ≥3 diagnoses/year in 2017. We assessed the full extent, composition, and transmission rates of new and ongoing priority clusters. RESULTS: Full clusters were 3-9 times larger than detected clusters, with median detected cluster sizes in new and ongoing priority clusters of 4 (range 3-9) and 11 (range 3-33), respectively, corresponding to full cluster sizes with a median of 14 (3-74) and 94 (7-318), respectively. A median of 36.3% (range 11.1%-72.6%) of infections in the full new priority clusters were undiagnosed. HIV transmission rates in these clusters were >4 times the overall rate observed in the entire simulation. CONCLUSIONS: Priority clusters reflect networks with rapid HIV transmission. The substantially larger full extent of these clusters, high proportion of undiagnosed infections, and high transmission rates indicate opportunities for public health intervention and impact.

Public Health Response to Clusters of Rapid HIV Transmission Among Hispanic or Latino Gay, Bisexual, and Other Men Who Have Sex with Men - Metropolitan Atlanta, Georgia, 2021-2022.

During February 2021-June 2022, the Georgia Department of Public Health (GDPH) detected five clusters of rapid HIV transmission concentrated among Hispanic or Latino (Hispanic) gay, bisexual, and other men who have sex with men (MSM) in metropolitan Atlanta. The clusters were detected through routine analysis of HIV-1 nucleotide sequence data obtained through public health surveillance (1,2). Beginning in spring 2021, GDPH partnered with health districts with jurisdiction in four metropolitan Atlanta counties (Cobb, DeKalb, Fulton, and Gwinnett) and CDC to investigate factors contributing to HIV spread, epidemiologic characteristics, and transmission patterns. Activities included review of surveillance and partner services interview data,† medical chart reviews, and qualitative interviews with service providers and Hispanic MSM community members. By June 2022, these clusters included 75 persons, including 56% who identified as Hispanic, 96% who reported male sex at birth, 81% who reported male-to-male sexual contact, and 84% of whom resided in the four metropolitan Atlanta counties. Qualitative interviews identified barriers to accessing HIV prevention and care services, including language barriers, immigration- and deportation-related concerns, and cultural norms regarding sexuality-related stigma. GDPH and the health districts expanded coordination, initiated culturally concordant HIV prevention marketing and educational activities, developed partnerships with organizations serving Hispanic communities to enhance outreach and services, and obtained funding for a bilingual patient navigation program with academic partners to provide staff members to help persons overcome barriers and understand the health care system. HIV molecular cluster detection can identify rapid HIV transmission among sexual networks involving ethnic and sexual minority groups, draw attention to the needs of affected populations, and advance health equity through tailored responses that address those needs.

Clusters of Rapid HIV Transmission Among Gay, Bisexual, and Other Men Who Have Sex with Men - United States, 2018-2021.

Gay, bisexual, and other men who have sex with men (MSM) accounted for 68% of new HIV diagnoses in the United States in 2020* (1). Despite advances in treatment and prevention, HIV transmission among MSM continues, in part because of stigma and barriers to accessing prevention and treatment services (2). HIV cluster detection and response, a core strategy of the Ending the HIV Epidemic in the United States initiative,† is an important tool for early identification and response to rapid HIV transmission, including among MSM. To better understand rapid HIV transmission among this population, CDC characterized large HIV molecular clusters detected using analysis of HIV-1 nucleotide sequence data from the National HIV Surveillance System (NHSS).§ Among 38 such clusters first detected during 2018-2019 that had grown to include more than 25 persons by December 2021, 29 occurred primarily among MSM. Clusters primarily among MSM occurred in all geographic regions, and 97% involved multiple states. Clusters were heterogeneous in age, gender identity, and race and ethnicity and had rapid growth rates (median = nine persons added per year). The overall transmission rate at cluster detection was 22 transmission events per 100 person-years, more than six times that of previously estimated national transmission rates (3). Most clusters of rapid HIV transmission occur among MSM. Swift response to reach diverse persons and communities with early, tailored, and focused interventions is essential to reducing HIV transmission (4).

Molecular surveillance for large outbreaks of tuberculosis in the United States, 2014-2018.

OBJECTIVE: This study describes characteristics of large tuberculosis (TB) outbreaks in the United States detected using novel molecular surveillance methods during 2014-2016 and followed for 2 years through 2018. METHODS: We developed 4 genotype-based detection algorithms to identify large TB outbreaks of ≥10 cases related by recent transmission during a 3-year period. We used whole-genome sequencing and epidemiologic data to assess evidence of recent transmission among cases. RESULTS: There were 24 large outbreaks involving 518 cases; patients were primarily U.S.-born (85.1%) racial/ethnic minorities (84.1%). Compared with all other TB patients, patients associated with large outbreaks were more likely to report substance use, homelessness, and having been diagnosed while incarcerated. Most large outbreaks primarily occurred within residences among families and nonfamilial social contacts. A source case with a prolonged infectious period and difficulties in eliciting contacts were commonly reported contributors to transmission. CONCLUSION: Large outbreak surveillance can inform targeted interventions to decrease outbreak-associated TB morbidity.

Baseline HIV drug-resistance testing: 12 US jurisdictions, 2014-2019.

OBJECTIVE: To understand recent patterns in reported baseline HIV drug-resistance testing over time in the United States. DESIGN: Data from the National HIV Surveillance System for persons who were aged at least 13 years at the time of HIV diagnosis during 2014-2019 and resided in one of 12 US jurisdictions with high levels of reporting in 2014 and 2015. METHODS: Among persons included in the analysis, we calculated the total proportion of HIV diagnoses occurring during 2014-2019 with a reported baseline sequence by year of diagnosis and sequence type. A baseline sequence was defined as any protease/ reverse transcriptase (PR/RT) or integrase sequence generated from a specimen collected 90 days or less after diagnosis. RESULTS: During 2014-2019, reported levels of baseline PR/RT (with or without integrase) testing varied by year from 46.9% to 51.8% without any clear pattern over time. PR/RT with integrase testing increased (8.3-19.4%) and integrase-only testing remained low (1.9-1.3%). CONCLUSION: While reported levels of baseline PR/RT (with or without integrase) testing have remained sufficiently high for the purposes of molecular cluster detection, higher levels would strengthen jurisdictions' and the Centers for Disease Control and Prevention's ability to monitor trends in HIV drug-resistance and detect and respond to HIV molecular clusters. Efforts to increase levels of reported baseline testing likely need to address both gaps in testing as well as reporting.

The CDC HIV Outbreak Coordination Unit: Developing a Standardized, Collaborative Approach to HIV Outbreak Assessment and Response.

The Centers for Disease Control and Prevention (CDC) and state, territorial, and local health departments have expanded efforts to detect and respond to HIV clusters and outbreaks in the United States. In July 2017, CDC created the HIV Outbreak Coordination Unit (OCU) to ensure consistent and collaborative assessment of requests from health departments for consultation or support on possible HIV clusters and outbreaks of elevated concern. The HIV OCU is a multidisciplinary, cross-organization functional unit within CDC's Division of HIV/AIDS Prevention. HIV OCU members have expertise in areas such as outbreak detection and investigation, prevention, laboratory services, surveillance and epidemiology, policy, communication, and operations. HIV OCU discussions facilitate problem solving, coordination, and situational awareness. Between HIV OCU meetings, designated CDC staff members communicate regularly with health departments to provide support and assessment. During July 2017-December 2019, the HIV OCU reviewed 31 possible HIV clusters and outbreaks (ie, events) in 22 states that were detected by CDC, health departments, or local partners; 17 events involved HIV transmission associated with injection drug use, and other events typically involved sexual transmission or overall increases in HIV diagnoses. CDC supported health departments remotely or on site with planning and prioritization; data collection, management, and analysis; communications; laboratory support; multistate coordination; and expansion of HIV prevention services. The HIV OCU has augmented CDC's support of HIV cluster and outbreak assessment and response at health departments and had important internal organizational benefits. Health departments may benefit from developing or strengthening similar units to coordinate detection and response efforts within and across public health agencies and advance the national Ending the HIV Epidemic initiative.

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.

Genomic Surveillance for SARS-CoV-2 Variants Circulating in the United States, December 2020-May 2021.

SARS-CoV-2, the virus that causes COVID-19, is constantly mutating, leading to new variants (1). Variants have the potential to affect transmission, disease severity, diagnostics, therapeutics, and natural and vaccine-induced immunity. In November 2020, CDC established national surveillance for SARS-CoV-2 variants using genomic sequencing. As of May 6, 2021, sequences from 177,044 SARS-CoV-2-positive specimens collected during December 20, 2020-May 6, 2021, from 55 U.S. jurisdictions had been generated by or reported to CDC. These included 3,275 sequences for the 2-week period ending January 2, 2021, compared with 25,000 sequences for the 2-week period ending April 24, 2021 (0.1% and 3.1% of reported positive SARS-CoV-2 tests, respectively). Because sequences might be generated by multiple laboratories and sequence availability varies both geographically and over time, CDC developed statistical weighting and variance estimation methods to generate population-based estimates of the proportions of identified variants among SARS-CoV-2 infections circulating nationwide and in each of the 10 U.S. Department of Health and Human Services (HHS) geographic regions.* During the 2-week period ending April 24, 2021, the B.1.1.7 and P.1 variants represented an estimated 66.0% and 5.0% of U.S. SARS-CoV-2 infections, respectively, demonstrating the rise to predominance of the B.1.1.7 variant of concern† (VOC) and emergence of the P.1 VOC in the United States. Using SARS-CoV-2 genomic surveillance methods to analyze surveillance data produces timely population-based estimates of the proportions of variants circulating nationally and regionally. Surveillance findings demonstrate the potential for new variants to emerge and become predominant, and the importance of robust genomic surveillance. Along with efforts to characterize the clinical and public health impact of SARS-CoV-2 variants, surveillance can help guide interventions to control the COVID-19 pandemic in the United States.

Increasing Capacity to Detect Clusters of Rapid HIV Transmission in Varied Populations-United States.

Molecular cluster detection analyzes HIV sequences to identify rapid HIV transmission and inform public health responses. We describe changes in the capability to detect molecular clusters and in geographic variation in transmission dynamics. We examined the reporting completeness of HIV-1 polymerase sequences in quarterly National HIV Surveillance System datasets from December 2015 to December 2019. Priority clusters were identified quarterly. To understand populations recently affected by rapid transmission, we described the transmission risk and race/ethnicity of people in clusters first detected in 2018-2019. During December 2015 to December 2019, national sequence completeness increased from 26% to 45%. Of the 1212 people in the 136 clusters first detected in 2018-2019, 69% were men who have sex with men (MSM) and 11% were people who inject drugs (PWID). State-by-state analysis showed substantial variation in transmission risk and racial/ethnic groups in clusters of rapid transmission. HIV sequence reporting has increased nationwide. Molecular cluster analysis identifies rapid transmission in varied populations and identifies emerging patterns of rapid transmission in specific population groups, such as PWID, who, in 2015-2016, comprised only 1% of people in such molecular clusters. These data can guide efforts to focus, tailor, and scale up prevention and care services for these populations.

Progression and transmission of HIV (PATH 4.0)-A new agent-based evolving network simulation for modeling HIV transmission clusters.

We present the Progression and Transmission of HIV (PATH 4.0), a simulation tool for analyses of cluster detection and intervention strategies. Molecular clusters are groups of HIV infections that are genetically similar, indicating rapid HIV transmission where HIV prevention resources are needed to improve health outcomes and prevent new infections. PATH 4.0 was constructed using a newly developed agent-based evolving network modeling (ABENM) technique and evolving contact network algorithm (ECNA) for generating scale-free networks. ABENM and ECNA were developed to facilitate simulation of transmission networks for low-prevalence diseases, such as HIV, which creates computational challenges for current network simulation techniques. Simulating transmission networks is essential for studying network dynamics, including clusters. We validated PATH 4.0 by comparing simulated projections of HIV diagnoses with estimates from the National HIV Surveillance System (NHSS) for 2010-2017. We also applied a cluster generation algorithm to PATH 4.0 to estimate cluster features, including the distribution of persons with diagnosed HIV infection by cluster status and size and the size distribution of clusters. Simulated features matched well with NHSS estimates, which used molecular methods to detect clusters among HIV nucleotide sequences of persons with HIV diagnosed during 2015-2017. Cluster detection and response is a component of the U.S. Ending the HIV Epidemic strategy. While surveillance is critical for detecting clusters, a model in conjunction with surveillance can allow us to refine cluster detection methods, understand factors associated with cluster growth, and assess interventions to inform effective response strategies. As surveillance data are only available for cases that are diagnosed and reported, a model is a critical tool to understand the true size of clusters and assess key questions, such as the relative contributions of clusters to onward transmissions. We believe PATH 4.0 is the first modeling tool available to assess cluster detection and response at the national-level and could help inform the national strategic plan.

Geographic Distribution of HIV Transmission Networks in the United States.

BACKGROUND: Understanding geographic patterns of HIV transmission is critical to designing effective interventions. We characterized geographic proximity by transmission risk and urban-rural characteristics among people with closely related HIV strains suggestive of potential transmission relationships. METHODS: We analyzed US National HIV Surveillance System data of people diagnosed between 2010 and 2016 with a reported HIV-1 partial polymerase nucleotide sequence. We used HIV TRAnsmission Cluster Engine (HIV-TRACE) to identify sequences linked at a genetic distance of ≤0.5%. For each linked person, we assessed median distances between counties of residence at diagnosis by transmission category and urban-rural classification, weighting observations to account for persons with multiple linked sequences. RESULTS: There were 24,743 persons with viral sequence linkages to at least one other person included in this analysis. Overall, half (50.9%) of persons with linked viral sequences resided in different counties, and the median distance from persons with linked viruses was 11 km/7 miles [interquartile range (IQR), 0-145 km/90 miles]. Median distances were highest for men who have sex with men (MSM: 14 km/9 miles; IQR, 0-179 km/111 miles) and MSM who inject drugs, and median distances increased with increasing rurality (large central metro: 0 km/miles; IQR, 0-83 km/52 miles; nonmetro: 103 km/64 miles; IQR, 40 km/25 miles-316 km/196 miles). CONCLUSION: Transmission networks in the United States involving MSM, MSM who inject drugs, or persons living in small metro and nonmetro counties may be more geographically dispersed, highlighting the importance of coordinated health department efforts for comprehensive follow-up and linkage to care.

Incident infection in high-priority HIV molecular transmission clusters in the United States.

OBJECTIVE: To identify correlates of incident HIV infection in rapidly growing HIV molecular clusters. DESIGN: Phylogenetic analysis of HIV public health surveillance data. METHODS: High-priority HIV genetic transmission clusters with evidence of rapid growth in 2012 (i.e. clusters with a pairwise genetic distance ≤0.005 substitutions/site and at least three cases diagnosed in 2012) were identified using HIV-TRACE. Then, we investigated cluster growth, defined as HIV cases diagnosed in the following 5 years that were genetically linked to these clusters. For clusters that grew during the follow-up period, Bayesian molecular clock phylogenetic inference was performed to identify clusters with evidence of incident HIV infection (as opposed to diagnosis of previously infected cases) during this follow-up period. RESULTS: Of the 116 rapidly growing clusters identified, 73 (63%) had phylogenetic evidence for an incident HIV case during the 5-year follow-up period. Correlates of an incident HIV case arising in clusters included a greater number of diagnosed but virally unsuppressed cases in 2012, a greater number of inferred undiagnosed cases in the cluster in 2012, and a younger time of most recent common ancestor for the cluster. CONCLUSION: These findings suggest that incident infections in rapidly growing clusters originate equally from diagnosed but unsuppressed cases and undiagnosed infections. These results highlight the importance of promoting retention in care and viral suppression as well as partner notification and other case-finding activities when investigating and intervening on high-priority molecular transmission clusters.

Brief Report: Temporal Changes in HIV Transmission Patterns Among Young Men Who Have Sex With Men, United States, 2009-2016.

BACKGROUND: In the United States, young men (aged 13-24 years) who have sex with men (MSM) bear a disproportionate burden of HIV. Transmission among MSM has been found to be disassortative by age. METHODS: We analyzed HIV-1 pol sequences reported to the US National HIV Surveillance System from MSM with HIV diagnosed during 2009-2016. Using an HIV genetic transmission network, we identified persons with closely related viruses (ie, genetic distance ≤1.5%) and used multivariable logistic regression to examine changes from 2009-2012 to 2013-2016 in proportions of MSM linked to young MSM who were >5 years older or of the same race/ethnicity. RESULTS: Among 9510 young MSM linked to another MSM with a closely related virus, 37% linked to an older MSM and 62% linked to an MSM of the same race/ethnicity. Comparing 2013-2016 with 2009-2012, we found increases in linkage of older MSM to young MSM, with the most substantial increases seen in Hispanic/Latinos aged 13-19 [adjusted prevalence ratio (APR) = 1.31, 95% confidence interval (CI) = 1.11 to 1.56] and blacks aged 13-19 (APR = 1.23, CI = 1.06 to 1.41) and 20-24 years (APR = 1.14, CI = 1.02 to 1.28). By contrast, change in linkage patterns among racial/ethnic groups was unremarkable. CONCLUSIONS: We found evidence of increased age mixing among MSM with respect to HIV transmission over time, which coincides temporally with changes in partner-seeking behavior such as increased use of mobile applications. These findings indicate the importance of social factors on HIV sexual and transmission networks and suggest that prevention efforts need to effectively reach MSM of all ages.

Novel Method for Rapid Detection of Spatiotemporal HIV Clusters Potentially Warranting Intervention.

Rapid detection of increases in HIV transmission enables targeted outbreak response efforts to reduce the number of new infections. We analyzed US HIV surveillance data and identified spatiotemporal clusters of diagnoses. This systematic method can help target timely investigations and preventive interventions for maximum public health benefit.

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.

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.