Journal
JAIDS-JOURNAL OF ACQUIRED IMMUNE DEFICIENCY SYNDROMES
Volume 75, Issue 5, Pages 548-553Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/QAI.0000000000001429
Keywords
HIV continuum of care; queueing model; operations research; antiretroviral therapy; viral suppression
Categories
Funding
- National Institutes of Health [U01AI068636, U01AI069432, U01AI069434, U01AI103390, U01AI103397, U01AI103401, U01AI103408, U01DA036935, U01HD032632, U10EY008057, U10EY008052, U10EY008067, U24AA020794, U54MD007587, UL1RR024131, UL1TR000004, UL1TR000083, UL1TR000454, UM1AI035043, Z01CP010214, Z01CP010176]
- Centers for Disease Control and Prevention, USA [CDC-200-2006-18797, CDC-200-2015-63931]
- Agency for Healthcare Research and Quality, USA [90047713]
- Health Resources and Services Administration, USA [90051652]
- Canadian Institutes of Health Research, Canada [CBR-86906, CBR-94036, HCP-97105, TGF-96118]
- Ontario Ministry of Health and Long Term Care
- Government of Alberta, Canada
- Intramural Research Program of the National Cancer Institute
- Public Health Services and Systems Research Award for Predoctoral and Postdoctoral Scholars in Public Health Delivery
- University of Kentucky Research Foundation
- NIMH [R01 MH105203]
- NIDA [R01DA015612]
- National Institutes of Health grants [P30AI110527, P30MH62246, R01AA016893, R01CA165937, R01DA004334, R01DA011602, R01DA012568, R24AI067039, U01AA013566, U01AA020790, U01AI031834, U01AI034989, U01AI034993, U01AI034994, U01AI035004, U01AI035039, U01AI035040, U01AI035041, U01AI035042, U01AI037613]
- NIH [U01AI037984, U01AI038855, U01AI038858, U01AI042590, U01AI068634, U01AI069918, F31DA037788, G12MD007583, K01AI093197, K23EY013707, K24DA000432, K24AI065298, KL2TR000421, M01RR000052, N02CP055504, P30AI027757, P30AI027763, P30AI027767, P30AI036219, P30AI050410, P30AI094189]
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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.
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