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J Acquir Immune Defic Syndr. 2017 Aug 15;75(5):548-553. doi: 10.1097/QAI.0000000000001429.

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

Author information

1
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.

Abstract

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.

PMID:
28471841
PMCID:
PMC5533168
[Available on 2018-08-15]
DOI:
10.1097/QAI.0000000000001429
[Indexed for MEDLINE]

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