Dataset Information

The Effect of Female Sex on Hepatitis C Incidence Among People Who Inject Drugs: Results From the International Multicohort InC3 Collaborative.

ABSTRACT: Background:The objective of this study was to assess differences in hepatitis C virus (HCV) incidence by sex in people who inject drugs (PWID), using a large international multicohort set of pooled biological and behavioral data from prospective observational studies of incident human immunodeficiency virus (HIV) and HCV infections in high-risk cohorts (the InC3 Collaborative). Methods:HCV infection date was estimated based on a hierarchy of successive serological (anti-HCV), virological (HCV RNA), and clinical (symptoms and/or liver function tests) data. We used a Cox proportional hazards model to calculate the crude and adjusted female to male (F:M) hazard ratio (HR) for HCV incidence using biological sex as the main exposure. Results:A total of 1868 PWID were observed over 3994 person-years of observation (PYO). Unadjusted F:M HR was 1.38 (95% confidence interval [CI], 1.15-1.65) and remained significant after adjusting for behavioral and demographic risk factors (1.39 [95% CI, 1.12-1.72]). Although syringe and equipment sharing were associated with the highest HCV incidence rate in women (41.62 and 36.83 PYO, respectively), we found no sex differences attributed to these risk factors. Conclusions:Our findings indicate that women who inject drugs may be at greater risk of HCV acquisition than men, independent of demographic characteristics and risk behaviors. Multiple factors, including biological (hormonal), social network, and differential access to prevention services, may contribute to increased HCV susceptibility in women who inject drugs.

SUBMITTER: Esmaeili A

PROVIDER: S-EPMC5850635 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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The Effect of Female Sex on Hepatitis C Incidence Among People Who Inject Drugs: Results From the International Multicohort InC3 Collaborative.

Esmaeili Aryan A Mirzazadeh Ali A Morris Meghan D MD Hajarizadeh Behzad B Sacks Henry S HS Maher Lisa L Grebely Jason J Kim Arthur Y AY Lauer Georg G Cox Andrea L AL Hellard Margaret M Dietze Paul P Bruneau Julie J Shoukry Naglaa H NH Dore Gregory J GJ Lloyd Andrew R AR Prins Maria M Page Kimberly K

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 20180101 1

<h4>Background</h4>The objective of this study was to assess differences in hepatitis C virus (HCV) incidence by sex in people who inject drugs (PWID), using a large international multicohort set of pooled biological and behavioral data from prospective observational studies of incident human immunodeficiency virus (HIV) and HCV infections in high-risk cohorts (the InC3 Collaborative).<h4>Methods</h4>HCV infection date was estimated based on a hierarchy of successive serological (anti-HCV), viro ...[more]

PMID: 29020200

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Project description:ImportanceThe success of direct-acting antiviral therapies for chronic hepatitis C virus (HCV) infection led the World Health Organization to set elimination targets by 2030. For the United States to achieve these benchmarks, public health responses must target high-risk populations, such as people who inject drugs (PWID), a group with high rates of HCV incidence and low rates of treatment uptake.ObjectiveTo evaluate potential improvements in the HCV care cascade among PWID, focusing on improved testing, treatment uptake, and access to harm reduction.Design, setting, and participantsThis decision analytic model used a differential equation-based dynamic transmission model based on data from New Hampshire, an illustrative state with a large number of PWID and limited HCV treatment infrastructure. Surveillance data through 2020 was used for model parameterization, and the final analysis was conducted in May 2021.Main outcomes and measuresModel forecasts of chronic HCV cases and advanced-stage HCV outcomes from 2022 to 2045.ResultsA total of 6 scenarios were tested: (1) the base case, (2) improved harm reduction, (3) improved testing, (4) improved treatment, (5) improved testing and treatment, and (6) improved testing, treatment, and harm reduction. All scenarios with improved testing, treatment uptake, and/or access to harm reduction were associated with decreases in forecasted HCV prevalence and HCV-associated mortality compared with the base case. Improving harm reduction, testing, and treatment individually were forecast to reduce prevalence of HCV in 2045 from 69.7% in the base case to 62.8%, 45.7%, and 35.5%, respectively. Combining treatment and testing improvements was associated with a 2045 prevalence of 0.3%; adding harm reduction improvements was associated with further reductions in prevalence forecasts (to 0.2%), with fewer total treatments (10 960 vs 13 219 from 2022-2045).Conclusions and relevanceIn this modeling study, no single intervention was projected to achieve World Health Organization HCV elimination targets. Scenarios with improvements in both testing and treatment were associated with a prevalence of less than 3% by 2030 and achieved elimination targets. Adding improvements in harm reduction was associated with faster reductions in prevalence and fewer treatments.

Project description:Hepatitis C virus (HCV) chronically infects over 180 million people worldwide, with over 350,000 estimated deaths attributed yearly to HCV-related liver diseases. It disproportionally affects people who inject drugs (PWID). Currently there is no preventative vaccine and interventions feature long treatment durations with severe side-effects. Upcoming treatments will improve this situation, making possible large-scale treatment interventions. How these strategies should target HCV-infected PWID remains an important unanswered question. Previous models of HCV have lacked empirically grounded contact models of PWID. Here we report results on HCV transmission and treatment using simulated contact networks generated from an empirically grounded network model using recently developed statistical approaches in social network analysis. Our HCV transmission model is a detailed, stochastic, individual-based model including spontaneously clearing nodes. On transmission we investigate the role of number of contacts and injecting frequency on time to primary infection and the role of spontaneously clearing nodes on incidence rates. On treatment we investigate the effect of nine network-based treatment strategies on chronic prevalence and incidence rates of primary infection and re-infection. Both numbers of contacts and injecting frequency play key roles in reducing time to primary infection. The change from "less-" to "more-frequent" injector is roughly similar to having one additional network contact. Nodes that spontaneously clear their HCV infection have a local effect on infection risk and the total number of such nodes (but not their locations) has a network wide effect on the incidence of both primary and re-infection with HCV. Re-infection plays a large role in the effectiveness of treatment interventions. Strategies that choose PWID and treat all their contacts (analogous to ring vaccination) are most effective in reducing the incidence rates of re-infection and combined infection. A strategy targeting infected PWID with the most contacts (analogous to targeted vaccination) is the least effective.

Dataset Information

The Effect of Female Sex on Hepatitis C Incidence Among People Who Inject Drugs: Results From the International Multicohort InC3 Collaborative.

Publications

The Effect of Female Sex on Hepatitis C Incidence Among People Who Inject Drugs: Results From the International Multicohort InC3 Collaborative.

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