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Identifying Clusters of Recent and Rapid HIV Transmission Through Analysis of Molecular Surveillance Data

Oster, Alexandra M., MD*; France, Anne Marie, PhD*; Panneer, Nivedha, MPH*; Bañez Ocfemia, M. Cheryl, MPH*; Campbell, Ellsworth, MS*; Dasgupta, Sharoda, PhD*,†; Switzer, William M., MPH*; Wertheim, Joel O., PhD; Hernandez, Angela L., MD, MPH*

JAIDS Journal of Acquired Immune Deficiency Syndromes: December 15, 2018 - Volume 79 - Issue 5 - p 543–550
doi: 10.1097/QAI.0000000000001856
Epidemiology

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.

*Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA;

Epidemic Intelligence Service, Centers for Disease Control and Prevention, Atlanta, GA; and

Department of Medicine, University of California, San Diego, CA

Correspondence to: Alexandra M. Oster, MD, Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, 1600 Clifton Road NE, MS E-47 Atlanta, GA 30329 (e-mail: AOster@cdc.gov).

J.O.W. was funded in part by an NIH-NIAID K01 Career Development Award (K01AI110181) and a contract from the Centers for Disease Control and Prevention (CDC).

Presented in part at the International HIV Transmission Workshop; October 15, 2016; Chicago, IL, and the International AIDS Society Conference on HIV Science; July 26, 2017; Paris, France.

The authors have no conflicts of interest to disclose. The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.

A.M.O. and A.M.F. contributed equally to this work.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.jaids.com).

Received May 03, 2018

Accepted August 03, 2018

Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.