Saturday, November 28, 2015

Optimizing Human Immunodeficiency Virus Testing Interventions for Men Who Have Sex With Men in the United States: A Modeling Study

Background. In the United States, public health recommendations for men who have sex with men (MSM) include testing for human immunodeficiency virus (HIV) at least annually. We model the impact of different possible HIV testing policies on HIV incidence in a simulated population parameterized to represent US MSM.

Methods. We used exponential random graph models to explore, among MSM, the short-term impact on baseline (under current HIV testing practices and care linkage) HIV incidence of the following: (1) increasing frequency of testing; (2) increasing the proportion who ever test; (3) increasing test sensitivity; (4) increasing the proportion of the diagnosed population achieving viral suppression; and combinations of 1–4. We simulated each scenario 20 times and calculated the median and interquartile range of 3-year cumulative incidence of HIV infection.

Results. The only intervention that reduced HIV incidence on its own was increasing the proportion of the diagnosed population achieving viral suppression; increasing frequency of testing, the proportion that ever test or test sensitivity did not appreciably reduce estimated incidence. However, in an optimal scenario in which viral suppression improved to 100%, HIV incidence could be reduced by an additional 17% compared with baseline by increasing testing frequency to every 90 days and test sensitivity to 22 days postinfection.

Conclusions.Increased frequency, coverage, or sensitivity of HIV testing among MSM is unlikely to result in reduced HIV incidence unless men diagnosed through enhanced testing programs are also engaged in effective HIV care resulting in viral suppression at higher rates than currently observed.

Below:  Population-level impact of increasing testing frequency on 3-year human immunodeficiency virus (HIV) incidence in a simulated population of 5250 men who have sex with men in the United States. Estimates are the median and ranges of total cases of HIV observed over 20 simulations of each scenario, where the frequency of testing was set to either the baseline distribution observed in our study population or the time interval indicated on the x-axis.



Below:  Synergistic effects of human immunodeficiency virus (HIV) testing frequency, test sensitivity, and viral suppression among the diagnosed population on the median 3-year total of circulating viral load, by HIV testing and diagnosis group of men who have sex with men in a simulated population. Scenario 1 increased HIV testing frequency to once every 90 days and held the percentage of the diagnosed population that achieve viral suppression to 43.4% and test sensitivity to 45 days as in the baseline scenario. Scenario 2 increases the proportion achieving viral suppression to 100% of the diagnosed population, while keeping the baseline scenario for testing frequency and test sensitivity. Scenario 3 increases both viral suppression (to 100% of the diagnosed population) and test sensitivity (to 22 days), while holding testing frequency to the baseline distribution. Scenario 4 increases testing frequency to every 90 days and increases viral suppression to 100%, while holding test sensitivity to 45 days. Scenario 5 optimizes all 3 strategies at once, with viral suppression increased to 100% of the diagnosed population, testing every 90 days, and a test with a 22-day window period.



Below:  Synergistic effects of human immunodeficiency virus (HIV) testing frequency, test sensitivity, and viral suppression among the diagnosed population on 3-year HIV incidence across 20 simulations of a population of 5250 men who have sex with men in the United States. Scenario 1 increased HIV testing frequency to once every 90 days and held the percentage of the diagnosed population that achieve viral suppression to 43.4% and test sensitivity to 45 days as in the baseline scenario. Scenario 2 increases the proportion achieving viral suppression to 100% of the diagnosed population, while keeping the baseline scenario for testing frequency and test sensitivity. Scenario 3 increases both viral suppression (to 100% of the diagnosed population) and test sensitivity (to 22 days), while hold testing frequency to the baseline distribution. Scenario 4 increases testing frequency to every 90 days and increases viral suppression to 100%, while holding test sensitivity to 45 days. Scenario 5 optimizes all 3 strategies at once, with viral suppression increased to 100% of the diagnosed population, testing every 90 days, and a test with a 22-day window period.



Full article at:   http://goo.gl/d632L8

1Department of Epidemiology, Laney Graduate School
2Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
Correspondence: Kevin P. Delaney, MPH, PhD, Department of Epidemiology, Emory University, 1518 Clifton Road NE, Mailstop: 1518-002-4AA (SPH: Epidemiology), Atlanta, GA 30322 (ude.yrome@naledpk).




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