ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2021
Identification of a Human Immunodeficiency Virus Type 1 and Neurosyphilis Cluster in Vermont.
Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Abstract
backgroundRates of syphilis in the United States have more than doubled over the last several decades, largely among men who have sex with men (MSM). Our study characterizes a cluster of neurosyphilis cases among people with human immunodeficiency virus 1 (HIV-1) in Vermont in 2017-2018.
methodsVermont Department of Health disease intervention specialists conduct interviews with newly diagnosed HIV-1 cases and pursue sexual networking analyses. Phylogenetic and network analyses of available Vermont HIV-1 polymerase (pol) sequences identified clusters of infection. Fishers-exact and independent t-tests were used to compare people with HIV-1 within or outside an identified cluster.
resultsBetween 1 January 2017 and 31 December 2018, 38 residents were diagnosed with HIV-1 infection. The mean age was 35.5 years, 79% were male and 82% were White. Risk factors for HIV-1 included MSM status (79%) and methamphetamine use (21%). Eighteen cases (49%) had HIV-1 viral loads (VLs) >100 000 copies/mL and 47% had CD4 cell counts <200/mm3. Eleven of the 38 (29%) had positive syphilis serology, including four (36%) with neurosyphilis. Sexual networking analysis revealed a ten-person cluster with higher VLs at diagnosis (90% with VLs > 100 000 copies/mL vs 33%, P = 0.015). Phylogenetic analysis of pol sequences showed a cluster of 14 cases with sequences that shared 98%-100% HIV-1 nucleotide identity.
conclusionsThis investigation of newly infected HIV-1 cases in Vermont led to identification of a cluster that appeared more likely to have advanced HIV-1 disease and neurosyphilis, supported by phylogenetic and network analyses.
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