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People who inject drugs (PWID) and HIV-infected men who have sex with men (MSM) are key risk groups for HCV transmission. Mathematical modeling studies can help elucidate what level and combination of prevention intervention scale-up is required to control or eliminate epidemics among these key populations. We discuss the evidence surrounding HCV prevention interventions and provide an overview of the mathematical modeling literature projecting the impact of scaled-up HCV prevention among PWID and HIV-infected MSM. Harm reduction interventions, such as opiate substitution therapy and needle and syringe programs, are effective in reducing HCV incidence among PWID. Modeling and limited empirical data indicate that HCV treatment could additionally be used for prevention. No studies have evaluated the effectiveness of behavior change interventions to reduce HCV incidence among MSM, but existing interventions to reduce HIV risk could be effective. Mathematical modeling and empirical data indicate that scale-up of harm reduction could reduce HCV transmission, but in isolation is unlikely to eliminate HCV among PWID. By contrast, elimination is possibly achievable through combination scale-up of harm reduction and HCV treatment. Similarly, among HIV-infected MSM, eliminating the emerging epidemics will likely require HCV treatment scale-up in combination with additional interventions to reduce HCV-related risk behaviors. In summary, elimination of HCV will likely require combination prevention efforts among both PWID and HIV-infected MSM populations. Further empirical research is required to validate HCV treatment as prevention among these populations, and to identify effective behavioral interventions to reduce HCV incidence among MSM.

Type

Journal article

Journal

AIDS reviews

Publication Date

04/2017

Volume

19

Pages

97 - 104

Addresses

Division of Global Public Health, University of California San Diego, California, USA.

Keywords

Humans, Hepatitis C, HIV Infections, Substance Abuse, Intravenous, Antiviral Agents, Homosexuality, Male, Models, Biological, Male