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

Sex Transm Dis. 2020 Feb;47(2):80-87. doi: 10.1097/OLQ.0000000000001094.


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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Adult
  • Cluster Analysis
  • Contact Tracing*
  • Female
  • HIV / genetics*
  • HIV Infections / diagnosis*
  • HIV Infections / epidemiology
  • Humans
  • Male
  • Middle Aged
  • Models, Statistical
  • North Carolina / epidemiology
  • Transgender Persons / statistics & numerical data
  • pol Gene Products, Human Immunodeficiency Virus / genetics*


  • pol Gene Products, Human Immunodeficiency Virus