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A Flow-Based Model of the HIV Care Continuum in the United States

Gonsalves, Gregg S. PhD*; Paltiel, A. David PhD; Cleary, Paul D. PhD; Gill, Michael J. MBChB, FRCPC, FACP; Kitahata, Mari M. MD, MPH§; Rebeiro, Peter F. PhD, MHS; Silverberg, Michael J. PhD, MPH; Horberg, Michael MD, MAS#; Abraham, Alison G. PhD**; Althoff, Keri N. PhD, MPH††; Moore, Richard MD, MHS‡‡; Bosch, Ronald J. PhD§§; Tang, Tian MS‖‖; Hall, H. Irene PhD¶¶; Kaplan, Edward H. PhD##

JAIDS Journal of Acquired Immune Deficiency Syndromes: 15 August 2017 - Volume 75 - Issue 5 - p 548–553
doi: 10.1097/QAI.0000000000001429
Implementation Science

Background: Understanding the flow of patients through the continuum of HIV care is critical to determine how best to intervene so that the proportion of HIV-infected persons who are on antiretroviral treatment and virally suppressed is as large as possible.

Methods: Using immunological and virological data from the Centers for Disease Control and Prevention and the North American AIDS Cohort Collaboration on Research and Design from 2009 to 2012, we estimated the distribution of time spent in and dropout probability from each stage in the continuum of HIV care. We used these estimates to develop a queueing model for the expected number of patients found in each stage of the cascade.

Results: HIV-infected individuals spend an average of about 3.1 months after HIV diagnosis before being linked to care, or dropping out of that stage of the continuum with a probability of 8%. Those who link to care wait an additional 3.7 months on average before getting their second set of laboratory results (indicating engagement in care) or dropping out of care with probability of almost 6%. Those engaged in care spent an average of almost 1 year before achieving viral suppression on antiretroviral therapy or dropping out with average probability 13%. For patients who achieved viral suppression, the average time suppressed on antiretroviral therapy was an average of 4.5 years.

Conclusions: Interventions should be targeted to more rapidly identifying newly infected individuals, and increasing the fraction of those engaged in care that achieves viral suppression.

Departments of *Epidemiology of Microbial Diseases; and

Health Policy and Management, Yale School of Public Health, New Haven, CT;

Department of Medicine, University of Calgary, Alberta, Canada;

§Center for AIDS Research, University of Washington, Seattle, WA;

Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN;

Division of Research, Kaiser Permanente Northern California, Oakland, CA;

#Kaiser Permanente Mid-Atlantic Permanente Research Institute, Rockville, MD;

**Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, MD;

††Department of Epidemiology, Johns Hopkins University, Baltimore, MD;

‡‡Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD;

§§Center for Biostatistics in AIDS Research, Harvard School of Public Health, Boston, MA;

‖‖ICF International, Atlanta, GA;

¶¶Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA; and

##Yale School of Management, Yale School of Public Health, Yale School of Engineering and Applied Science, Yale University, New Haven, CT.

Correspondence to: Gregg S. Gonsalves, PhD, Yale School of Public Health, Laboratory of Epidemiology and Public Health, 60 College Street, Suite 318, New Haven, CT 06510 (e-mail: gregg.gonsalves@yale.edu).

This work was supported by National Institutes of Health grants U01AI069918, F31DA037788, G12MD007583, K01AI093197, K23EY013707, K24DA000432, K24AI065298, KL2TR000421, M01RR000052, N02CP055504, P30AI027757, P30AI027763, P30AI027767, P30AI036219, P30AI050410, P30AI094189, P30AI110527, P30MH62246, R01AA016893, R01CA165937, R01DA004334, R01DA011602, R01DA012568, R24AI067039, U01AA013566, U01AA020790, U01AI031834, U01AI034989, U01AI034993, U01AI034994, U01AI035004, U01AI035039, U01AI035040, U01AI035041, U01AI035042, U01AI037613, U01AI037984, U01AI038855, U01AI038858, U01AI042590, U01AI068634, U01AI068636, U01AI069432, U01AI069434, U01AI103390, U01AI103397, U01AI103401, U01AI103408, U01DA036935, U01HD032632, U10EY008057, U10EY008052, U10EY008067, U24AA020794, U54MD007587, UL1RR024131, UL1TR000004, UL1TR000083, UL1TR000454, UM1AI035043, Z01CP010214, and Z01CP010176; contracts CDC-200-2006-18797 and CDC-200-2015-63931 from the Centers for Disease Control and Prevention, USA; contract 90047713 from the Agency for Healthcare Research and Quality, USA; contract 90051652 from the Health Resources and Services Administration, USA; grants CBR-86906, CBR-94036, HCP-97105, and TGF-96118 from the Canadian Institutes of Health Research, Canada; Ontario Ministry of Health and Long Term Care; and the Government of Alberta, Canada. Additional support was provided by the Intramural Research Program of the National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

G.S.G. was supported by a Public Health Services and Systems Research Award for Predoctoral and Postdoctoral Scholars in Public Health Delivery, from the University of Kentucky Research Foundation. A.D.P. is supported by NIMH R01 MH105203 and NIDA R01DA015612.

Keeneland Conference 2015: Using Public Health Research to Build an Effective, Efficient, and Equitable System; April 20–22, 2015; Lexington, KY.

The authors have no conflicts of interest to disclose.

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the U.S. Centers for Disease Control and Prevention.

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 January 09, 2017

Accepted April 12, 2017

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