Implementation of Syringe Services Programs to Prevent Rapid Human Immunodeficiency Virus Transmission in Rural Counties in the United States: A Modeling Study

Abstract

<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> <jats:p>Syringe services programs (SSPs) are effective venues for delivering harm-reduction services to people who inject drugs (PWID). However, SSPs often face significant barriers to implementation, particularly in the absence of known human immunodeficiency virus (HIV) outbreaks.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>Using an agent-based model, we simulated HIV transmission in Scott County, Indiana, a rural county with a 1.7% prevalence of injection drug use. We compared outcomes arising in the absence of an SSP, in the presence of a pre-existing SSP, and with implementation of an SSP after the detection of an HIV outbreak among PWID over 5 years following the introduction of a single infection into the network.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>In the absence of an SSP, the model predicted an average of 176 infections among PWID over 5 years or an incidence rate of 12.1/100 person-years. Proactive implementation averted 154 infections and decreased incidence by 90.3%. With reactive implementation beginning operations 10 months after the first infection, an SSP would prevent 107 infections and decrease incidence by 60.8%. Reductions in incidence were also observed among people who did not inject drugs.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>Based on model predictions, proactive implementation of an SSP in Scott County had the potential to avert more HIV infections than reactive implementation after the detection of an outbreak. The predicted impact of reactive SSP implementation was highly dependent on timely implementation after detecting the earliest infections. Consequently, there is a need for expanded proactive SSP implementation in the context of enhanced monitoring of outbreak vulnerability in Scott County and similar rural contexts.</jats:p> </jats:sec>

Network structure and rapid HIV transmission among people who inject drugs: A simulation-based analysis

“…We adapted a previously published version of the TITAN model, an agent-based network model, to explore rapid HIV transmission in Scott County, Indiana ( Goedel et al, 2019 ). Agent-based modeling is a simulation method that represents micro-level interactions between individual entities called agents to understand the emergence of macro-level trends.…”

“…The model was then calibrated to the observed number of incident HIV infections through the scaling of the frequency of injection acts ( Peters et al, 2016 ) (see HIV Transmission for further discussion). All parameter values, key assumptions, and sources were previously described in detail ( Goedel et al, 2019 ). New elements and calibration of the current model version are discussed here and are expanded upon in the Supplemental Appendix .…”

“…The transmission of HIV infection throughout the network has been previously described ( Goedel et al, 2019 ). Briefly, agents were assigned a target number of condomless vaginal intercourse acts per partner per month, drawn from a Poisson distribution with a mean of 13 acts per month ( Crosby et al, 2012 ).…”

“…The natural history of HIV infection, as well as engagement with HIV testing and treatment, were simulated as previously described ( Goedel et al, 2019 ). In 2015, an alert from the health department following 11 newly diagnosed infections began the contact tracing investigation and increased testing activities ( Peters et al, 2016 ).…”

“…This study aimed to extend this body of research and provide novel insights on injection networks in rural settings by characterizing the observed network from the HIV outbreak in Scott County, Indiana. Using a previously published, discrete-time, agent-based network model ( Goedel et al, 2019 ), we generated a series of stochastic networks with comparable numbers of individuals and ties between them to the observed network to investigate our model’s ability to produce the observed network structures, and how variability in these structures may impact HIV transmission.…”