Expedited partner therapy (EPT) is an intervention for patients with gonorrhea or chlamydia, providing index patients with prescriptions or medication to give to their partners. Expedited partner therapy is recommended for heterosexuals but not for men who have sex with men (MSM), partially due to concerns about overtreatment of uninfected partners and missed opportunities for human immunodeficiency virus (HIV) diagnosis.
We extended our stochastic network-based mathematical model of HIV, gonorrhea, and chlamydia among MSM to include EPT. The EPT implementation was simulated for 10 years. Counterfactual scenarios varied EPT coverage, provision, uptake, and partnership window duration. We estimated sexually transmitted infection (STI) incidence, proportion of infections averted, and process outcomes under each scenario.
Delivery of EPT to 20% of eligible MSM index patients (coverage) reduced cumulative STI incidence by 27% (interquartile range, 13%–39%) over 10 years compared with current estimated STI screening levels. A 20% increase in providing medication to non–index partners (provision) averted 32% (interquartile range, 20%–41%) of STI infections compared with estimated STI screening levels. When targeted by partnership type, EPT solely to casual partners maximized the population-level infections averted. The proportion of partners given medication who had no current STI varied from 52% to 63%, depending on coverage level. The proportion of partners given medication with undiagnosed HIV infection was 4% across scenarios.
Expedited partner therapy could reduce bacterial STI incidence for MSM. However, this intervention could result in missed opportunities for HIV/STI prevention and a substantial increase in use of antimicrobials by STI-uninfected MSM, raising concerns about cost and antimicrobial resistance.
Expedited partner therapy for US men who have sex with men could reduce bacterial STI incidence, but may increase antimicrobial use and result in missed opportunities for disease prevention.
From the *Department of Epidemiology, Emory University, Atlanta, GA
†Department of Global Health, University of Washington
‡HIV/STD Program, Public Health-Seattle & King County, Seattle, WA
§Division of STD Prevention, Centers for Disease Control and Prevention
¶Department of Medicine, Emory University, Atlanta, GA
∥Department of Epidemiology and Biostatistics, University of Albany, Albany, NY
Acknowledgments: The authors would like to thank the members of the scientific and public health advisory group of the Coalition for Applied Modeling for Prevention project for their input on this study, and specifically those members who reviewed a previous version of this manuscript: Thomas Bertrand, David Dowdy, and Gregory Felzien. This work was supported by Centers for Disease Control and Prevention [grant: U38 PS004646], the National Institutes of Health [grant number: R21 MH112449; grant: R01 AI138783], and the Center for AIDS Research at Emory University [grant: P30 AI050409]. The authors declare no conflicts of interest. The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the US National Institutes of Health or Centers for Disease Control and Prevention.
Conflict of Interest: None declared.
Sources of Funding: This work was supported by Centers for Disease Control and Prevention [grant number: U38 PS004646], the National Institutes of Health [grant number: R21 MH112449; grant number: R01 AI138783], and the Center for AIDS Research at Emory University [grant number: P30 AI050409].
Data Availability: All data and software code necessary to simulate these models and run these analyses are stored on GitHub at: http://github.com/statnet/EpiModelHIV (branch is EPT) and http://github.com/EpiModel/EPT.
Disclaimer: The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.
Correspondence: Kevin M. Weiss, MPH, Department of Epidemiology, Rollins School of Public Health, Emory University, 1518 Clifton Rd, Atlanta, GA 30322. E-mail: firstname.lastname@example.org; Samuel M. Jenness, PhD, Department of Epidemiology, Rollins School of Public Health, Emory University, 1518 Clifton Rd, Atlanta, GA 30322. E-mail: email@example.com.
Received for publication May 15, 2019, and accepted July 31, 2019.
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