Transmission Patterns in a Low HIV-Morbidity State - Wisconsin, 2014-2017.

Public health interviews (i.e., partner services), during which persons with diagnosed human immunodeficiency virus (HIV) infection name their sexual or needle-sharing partners (named partners), are used to identify HIV transmission networks to guide and prioritize HIV prevention activities. HIV sequence data, generated from provider-ordered drug resistance testing, can be used to understand characteristics of molecular clusters, a group of sequences for which each sequence is highly similar (linked) to all other sequences, and assess whether named partners are plausible HIV transmission partners. Although molecular data in higher HIV-morbidity states have been analyzed (1-3), few analyses exist for lower morbidity states (4), such as Wisconsin, which reported 4.6 HIV diagnoses per 100,000 persons aged ≥13 years in 2016 (5). The Wisconsin Division of Public Health (DPH) analyzed HIV sequence data generated from provider-ordered drug resistance testing and collected through routine HIV surveillance to identify molecular clusters and describe demographic and transmission risk characteristics among pairs of persons whose sequences were highly genetically similar (i.e., molecular linkages). In addition, overlap between partner linkages identified during public health interviews and molecular linkages was assessed. Overall, characteristics of molecular clusters in Wisconsin mirrored those from states with more HIV diagnoses, particularly in that most molecular linkages were observed among persons of the same race (78.2% of non-Hispanic blacks [blacks] linked to other blacks), the same transmission risk (90.2% of men who have sex with men [MSM] linked to other MSM), and the same age group (59.2% of persons aged 20-29 years linked to other persons aged 20-29 years). Among named partner linkages identified during interviews in which both persons also had a reported sequence, overlap of named partner and molecular linkages was moderate: 33.8% of named partners were plausible transmission partners according to available molecular data. Analysis of HIV sequence data is a useful tool for characterizing transmission patterns not immediately apparent using traditional public health interview data, even in a state with lower HIV morbidity. Prevention recommendations generated from national data (e.g., targeting preexposure prophylaxis for HIV-negative persons at high risk and implementing measures to maintain viral suppression among persons with HIV infection) also are relevant in a lower HIV-morbidity state.

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.

The International Dimension of the U.S. HIV Transmission Network and Onward Transmission of HIV Recently Imported into the United States.

The majority of HIV infections in the United States can be traced back to a single introduction in late 1960s or early 1970s. However, it remains unclear whether subsequent introductions of HIV into the United States have given rise to onward transmission. Genetic transmission networks can aid in understanding HIV transmission. We constructed a genetic distance-based transmission network using HIV-1 pol sequences reported to the U.S. National HIV Surveillance System (n = 41,539) and all publicly available non-U.S. HIV-1 pol sequences (n = 86,215). Of the 13,145 U.S. persons clustered in the network, 457 (3.5%) were genetically linked to a potential transmission partner outside the United States. For internationally connected persons residing in but born outside the United States, 61% had a connection to their country of birth or to another country that shared a language with their country of birth. Bayesian molecular clock phylogenetic analyses indicate that introduced nonsubtype B infections have resulted in onward transmission within the United States.

Young Sexual Minority Males in the United States: Sociodemographic Characteristics And Sexual Attraction, Identity and Behavior.

CONTEXT: HIV incidence is increasing among 13-24-year-old U.S. men who have sex with men, yet limited research is available to guide HIV prevention efforts for this population. METHODS: National Survey of Family Growth data collected in 2002, in 2006-2010 and in 2011-2013 from 8,068 males aged 15-24 were analyzed to describe the population of U.S. young sexual minority males (i.e., males reporting same-sex attraction, identity or behavior). Correlates of sexual minority classification were assessed in logistic regression models. RESULTS: An estimated 10% of young males, representing a population of 2.1 million, were sexual minorities. Males had an elevated likelihood of being sexual minorities if they were aged 18-19 or 20-24, rather than 15-17 (prevalence ratio, 1.7 for each); belonged to nonblack, non-Hispanic racial or ethnic minority groups (1.6); had no religious affiliation, rather than considering religion very important (1.9); or lived below the federal poverty level (1.3). They had a reduced likelihood of being sexual minorities if they lived in metropolitan areas outside of central cities (0.7). Among young sexual minority males, 44% were 15-19 years old, 29% were poor and 59% resided outside central cities. Forty-seven percent had engaged in same-sex behavior. Of those with data on all measured dimensions of sexuality, 24% reported same-sex attraction, identity and behavior; 22% considered themselves heterosexual, yet had had a male sex partner. CONCLUSION: Future investigations can further explore subpopulations of young sexual minority males and assess sexual trajectories, resilience and HIV risk.