title: HCV prevalence can predict HIV epidemic potential among people who inject drugs: mathematical modeling analysis.
abstract: Hepatitis C virus (HCV) and HIV are both transmitted through percutaneous exposures among people who inject drugs (PWID). Ecological analyses on global epidemiological data have identified a positive association between HCV and HIV prevalence among PWID. Our objective was to demonstrate how HCV prevalence can be used to predict HIV epidemic potential among PWID.
  
  
  
  Two population-level models were constructed to simulate the evolution of HCV and HIV epidemics among PWID. The models described HCV and HIV parenteral transmission, and were solved both deterministically and stochastically.
  
  
  
  The modeling results provided a good fit to the epidemiological data describing the ecological HCV and HIV association among PWID. HCV was estimated to be eight times more transmissible per shared injection than HIV. A threshold HCV prevalence of 29.0% (95% uncertainty interval (UI): 20.7-39.8) and 46.5% (95% UI: 37.6-56.6) were identified for a sustainable HIV epidemic (HIV prevalence >1%) and concentrated HIV epidemic (HIV prevalence >5%), respectively. The association between HCV and HIV was further described with six dynamical regimes depicting the overlapping epidemiology of the two infections, and was quantified using defined and estimated measures of association. Modeling predictions across a wide range of HCV prevalence indicated overall acceptable precision in predicting HIV prevalence at endemic equilibrium. Modeling predictions were found to be robust with respect to stochasticity and behavioral and biological parameter uncertainty. In an illustrative application of the methodology, the modeling predictions of endemic HIV prevalence in Iran agreed with the scale and time course of the HIV epidemic in this country.
  
  
  
  Our results show that HCV prevalence can be used as a proxy biomarker of HIV epidemic potential among PWID, and that the scale and evolution of HIV epidemic expansion can be predicted with sufficient precision to inform HIV policy, programming, and resource allocation.
authors.affiliation: Infectious Disease Epidemiology Group, Weill Cornell Medical College - Qatar, Cornell University, Doha, Qatar.|Department of Healthcare Policy and Research, Weill Cornell Medical College, Cornell University, New York, USA.
authors.forename: Vajiheh
authors.initials: V
authors.lastname: Akbarzadeh
authors.affiliation: Infectious Disease Epidemiology Group, Weill Cornell Medical College - Qatar, Cornell University, Doha, Qatar. Ghina.Mumtaz@lshtm.ac.uk.|Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK. Ghina.Mumtaz@lshtm.ac.uk.
authors.forename: Ghina R
authors.initials: GR
authors.lastname: Mumtaz
authors.affiliation: Infectious Disease Epidemiology Group, Weill Cornell Medical College - Qatar, Cornell University, Doha, Qatar.
authors.forename: Susanne F
authors.initials: SF
authors.lastname: Awad
authors.affiliation: Department of Infectious Disease Epidemiology, MRC Tropical Epidemiology Group, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
authors.forename: Helen A
authors.initials: HA
authors.lastname: Weiss
authors.affiliation: Infectious Disease Epidemiology Group, Weill Cornell Medical College - Qatar, Cornell University, Doha, Qatar. lja2002@qatar-med.cornell.edu.|Department of Healthcare Policy and Research, Weill Cornell Medical College, Cornell University, New York, USA. lja2002@qatar-med.cornell.edu.|College of Public Health, Hamad bin Khalifa University, Doha, Qatar. lja2002@qatar-med.cornell.edu.
authors.forename: Laith J
authors.initials: LJ
authors.lastname: Abu-Raddad
mesh_terms: D015984:Causality; D015897:Comorbidity; D015658:HIV Infections / Q000453:epidemiology* / Q000635:transmission; D006526:Hepatitis C / Q000453:epidemiology* / Q000635:transmission; D006801:Humans; D007492:Iran; D015233:Models, Statistical; D015995:Prevalence; D011379:Prognosis; D015819:Substance Abuse, Intravenous / Q000453:epidemiology*
keywords: HIV; Hepatitis C virus; Mathematical modeling; People who inject drugs; Prediction