Age-targeted HIV treatment and primary prevention as a ‘ring fence’ to efficiently interrupt the age patterns of transmission in generalized epidemic settings in South Africa

Abstract

BackgroundGeneralized HIV epidemics propagate to future generations according to the age patterns of transmission. We hypothesized that future generations could be protected from infection using age-targeted prevention, analogous to the ring-fencing strategies used to control the spread of smallpox.MethodsWe modeled age-targeted or cohort-targeted outreach with HIV treatment and/or prevention using EMOD-HIV v0·8, an individual-based network model of HIV transmission in South Africa.ResultsTargeting ages 20 to 30 with intensified outreach, linkage, and eligibility for antiretroviral therapy (ART) averted 45% as many infections as universal outreach for approximately one-fifth the cost beyond existing HIV services. Though cost-effective, targeting failed to eliminate all infections to those under 20 due to vertical and inter-generational transmission. Cost-effectiveness of optimal prevention strategies included US$6238 per infection averted targeting ages 10–30, US$5031 targeting 20–30, US$4279 targeting 22–27, and US$3967 targeting 25–27, compared to US$10 812 for full-population test-and-treat. Minimizing burden (disability-adjusted life years [DALYs]) rather than infections resulted in older target age ranges because older adults were more likely to receive a direct health benefit from treatment.ConclusionsAge-targeted treatment for HIV prevention is unlikely to eliminate HIV epidemics, but is an efficient strategy for reducing new infections in generalized epidemics settings.

Development of an oral once-weekly drug delivery system for HIV antiretroviral therapy

“…5i and j ). However, because weekly PrEP will likely involve different drugs and may influence efficacy and coverage in unexpected ways, a wider range of efficacy and coverage assumptions were input into the EMOD v2.5 microsimulation model 52 – 55 , a network transmission model of HIV that includes heterosexual and mother-to-child HIV transmission, calibrated to the HIV epidemic in South Africa 56 – 58 . Assuming that availability of a weekly regimen influences only efficacy and not coverage, the model predicts approximately 200,000 additional infections averted over 20 years—a 3.4% cumulative reduction—by making a weekly PrEP formulation available for high risk populations ages between 15 and 29.…”

Agent-based network model predicts strong benefits to youth-centered HIV treatment-as-prevention efforts

“…Our results come with the caveat that the failure to achieve 100% suppression is due primarily to steep drop-offs in steps in the treatment cascade that occur subsequent to testing. Under an alternative framework in which testing (rather than linkage to care) is the limiting factor, others have reported that targeting strategies may be optimally directed at slightly older people as prevalence tends to increase with age (Bershteyn et al 2016, Golden et al 2017. Another caveat is that our model ignores the tendency in many societies for younger women to partner with older men (Barbieri andHertrich 2005, Harling et al 2014).…”

“…(5) People are rarely infected by HIV+ partners under the age of 16 (Shisana et al 2014). These last two points suggest that treating people between 16 and 25 can result in "herd immunity" to teenagers entering the sexually active population (Bershteyn et al 2016).…”

“…Alsallaq et al (2017) note, however, that "youth-focused" prevention and treatment programs can outperform "adult-focused" programs, suggesting a more effective approach for HIV eradication. While age-based HIV testing has been modeled (Bershteyn et al 2016, Golden et al 2017), we are not aware of other models focused specifically on targeting youth for antiretroviral treatment.…”

Implementation and applications of EMOD, an individual-based multi-disease modeling platform

“…7 shows a typical ‘cascade of care’ used in the EMOD HIV model, and zooms into one component (infant HIV testing) to show how health seeking patterns are user-specified. The baseline model and resulting epidemic and treatment patterns have been systematically compared to country-specific data and other model estimates (Eaton et al 2012 , 2014 , 2015 ), and the ability to flexibly target sub-populations has been used to explore strategies for efficient resource allocation and interrupting onward transmission (Klein, Eckhoff and Bershteyn 2015 ; Bershteyn, Klein and Eckhoff 2016 ). The triggerable campaign framework has also been used to configure reactive case investigation in a spatial malaria model, where treatment of an index case in a household broadcasts the signal for case investigation only to neighboring households (Gerardin et al 2017 ).…”