Evidence map›Paper›PMID 36680108›Full record

ArticleViruses2022

Insights into HIV-1 Transmission Dynamics Using Routinely Collected Data in the Mid-Atlantic United States.

Seble G Kassaye, Zehava Grossman, Priyanka Vengurlekar, William Chai, Megan Wallace, Soo-Yon Rhee, William A Meyer, Harvey W Kaufman, Amanda Castel, Jeanne Jordan and 6 more

Abstract read
In one paragraph

Article in Viruses, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Seble G KassayeDepartment of Medicine, Georgetown University, Washington, DC 20057, USA.
Zehava GrossmanHIV Dynamics and Replication Program, National Cancer Institute, Frederick, MD 21702, USA.
Priyanka VengurlekarDepartment of Medicine, Georgetown University, Washington, DC 20057, USA.
William ChaiWarren Alpert Medical School, Brown University, Providence, RI 02912, USA.
Megan WallaceDepartment of Medicine, Georgetown University, Washington, DC 20057, USA.
Soo-Yon RheeDepartment of Medicine, Stanford University, Stanford, CA 94305, USA.ORCID 0000-0002-1231-6542
William A MeyerQuest Diagnostics, Secaucus, NJ 07094, USA.
Harvey W KaufmanQuest Diagnostics, Secaucus, NJ 07094, USA.
Amanda CastelDepartment of Epidemiology, George Washington University, Washington, DC 20052, USA.
Jeanne JordanDepartment of Epidemiology, George Washington University, Washington, DC 20052, USA.
Keith A CrandallComputational Biology Institute, George Washington University, Ashburn, VA 20147, USA.ORCID 0000-0002-0836-3389
Alisa KangDepartment of Medicine, Georgetown University, Washington, DC 20057, USA.
Princy KumarDepartment of Medicine, Georgetown University, Washington, DC 20057, USA.
David A KatzensteinDepartment of Medicine, Stanford University, Stanford, CA 94305, USA.
Robert W ShaferDepartment of Medicine, Stanford University, Stanford, CA 94305, USA.ORCID 0000-0003-2513-2643
Frank MaldarelliHIV Dynamics and Replication Program, National Cancer Institute, Frederick, MD 21702, USA.

Funding

WOMEN'S INTERAGENCY HIV STUDY (WIHS) ORGANIZATION AND MANAGEMENTU01AI034994 · NIAID · GEORGETOWN UNIVERSITY · PI KASSAYE, SEBLE G · 1993 to 2018
$52.0M
SWG 2: Cure Research Scientific Working GroupP30AI117970 · NIAID · GEORGE WASHINGTON UNIVERSITY · PI Italo Mocchetti · 2015 to 2026
$29.6M
HIV Drug Resistance DatabaseR24AI136618 · NIAID · STANFORD UNIVERSITY · PI ROBERT William SHAFER · 2018 to 2026
$7.3M
Georgetown-Howard Universities Center for Clinical and Translational Science (GHUKL2TR000102 · NCATS · GEORGETOWN UNIVERSITY · PI MELLMAN, THOMAS A, VERBALIS, JOSEPH G · 2012 to 2014
$1.7M
Georgetown-Howard Universities Center for Clinical and Translational Science (GHUKL2RR031974 · NCRR · GEORGETOWN UNIVERSITY · PI VERBALIS, JOSEPH G · 2010 to 2011
$711k
NCATS NIH HHS KL2 TR000102NCRR NIH HHS KL2 RR031974NIAID NIH HHS P30 AI117970NIAID NIH HHS R24 AI136618NIAID NIH HHS U01 AI034994
6 · The paper itself

Abstract

Background: Molecular epidemiological approaches provide opportunities to characterize HIV transmission dynamics. We analyzed HIV sequences and virus load (VL) results obtained during routine clinical care, and individual’s zip-code location to determine utility of this approach. Methods: HIV-1 pol sequences aligned using ClustalW were subtyped using REGA. A maximum likelihood (ML) tree was generated using IQTree. Transmission clusters with ≤3% genetic distance (GD) and ≥90% bootstrap support were identified using ClusterPicker. We conducted Bayesian analysis using BEAST to confirm transmission clusters. The proportion of nucleotides with ambiguity ≤0.5% was considered indicative of early infection. Descriptive statistics were applied to characterize clusters and group comparisons were performed using chi-square or t-test. Results: Among 2775 adults with data from 2014−2015, 2589 (93%) had subtype B HIV-1, mean age was 44 years (SD 12.7), 66.4% were male, and 25% had nucleotide ambiguity ≤0.5. There were 456 individuals in 193 clusters: 149 dyads, 32 triads, and 12 groups with ≥ four individuals per cluster. More commonly in clusters were males than females, 349 (76.5%) vs. 107 (23.5%), p < 0.0001; younger individuals, 35.3 years (SD 12.1) vs. 44.7 (SD 12.3), p < 0.0001; and those with early HIV-1 infection by nucleotide ambiguity, 202/456 (44.3%) vs. 442/2133 (20.7%), p < 0.0001. Members of 43/193 (22.3%) of clusters included individuals in different jurisdictions. Clusters ≥ four individuals were similarly found using BEAST. HIV-1 viral load (VL) ≥3.0 log10 c/mL was most common among individuals in clusters ≥ four, 18/21, (85.7%) compared to 137/208 (65.8%) in clusters sized 2−3, and 927/1169 (79.3%) who were not in a cluster (p < 0.0001). Discussion: HIV sequence data obtained for HIV clinical management provide insights into regional transmission dynamics. Our findings demonstrate the additional utility of HIV-1 VL data in combination with phylogenetic inferences as an enhanced contact tracing tool to direct HIV treatment and prevention services. Trans-jurisdictional approaches are needed to optimize efforts to end the HIV epidemic.

Indexed as

HIV-1HIV InfectionsHIV SeropositivityAdultBayes TheoremCluster AnalysisFemaleGenotypeHumansMaleMolecular EpidemiologyPhylogenyRoutinely Collected Health DataUnited Statesclinical and phylogenetic data combinedcontact tracing toolHIV drug resistancemolecular epidemiologyphylogenetic analysisregional transmission dynamicstransmission networks

Identifiers

PMID36680108
PMCPMC9863702

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.