Impact of Hepatitis C Treatment as Prevention for People Who Inject Drugs is sensitive to contact network structure

Abstract

Treatment as Prevention (TasP) using directly-acting antivirals has been advocated for Hepatitis C Virus (HCV) in people who inject drugs (PWID), but treatment is expensive and TasP’s effectiveness is uncertain. Previous modelling has assumed a homogeneously-mixed population or a static network lacking turnover in the population and injecting partnerships. We developed a transmission-dynamic model on a dynamic network of injecting partnerships using data from survey of injecting behaviour carried out in London, UK. We studied transmission on a novel exponential-clustered network, as well as on two simpler networks for comparison, an exponential unclustered and a random network, and found that TasP’s effectiveness differs markedly. With respect to an exponential-clustered network, the random network (and homogeneously-mixed population) overestimate TasP’s effectiveness, whereas the exponential-unclustered network underestimates it. For all network types TasP’s effectiveness depends on whether treated patients change risk behaviour, and on treatment coverage: higher coverage requires fewer total treatments for the same health gain. Whilst TasP can greatly reduce HCV prevalence, incidence of infection, and incidence of reinfection in PWID, assessment of TasP’s effectiveness needs to take account of the injecting-partnership network structure and post-treatment behaviour change, and further empirical study is required.

Assessing uncertainty in the burden of hepatitis C virus: Comparison of estimated disease burden and treatment costs in the UK

“…People who inject drugs (PWID) are an important risk group for HCV infection and make up the majority of those infected with HCV in the UK . Treatment as Prevention (TasP) has been advocated for this group, although there are important uncertainties in its likely effectiveness, which depends upon the structure of the network of injecting partners, the coverage of testing and proportion of infected patients who are treated and cured, and the frequency of behaviour change preventing re‐infection after successful treatment . Further studies of all of these are required, including analysis of potential synergistic effects of combining HCV TasP with needle and syringe programmes (which reduce the infection risk of injecting) and opiate substitution therapy (which assist PWID in stopping injecting).…”

“…Further studies of all of these are required, including analysis of potential synergistic effects of combining HCV TasP with needle and syringe programmes (which reduce the infection risk of injecting) and opiate substitution therapy (which assist PWID in stopping injecting). Use of transmission dynamic models to analyse surveillance data is important in assessing population‐level impacts of interventions; in the case of PWID, it is important to use models that account for the injecting network structure and dynamics . In some marginalised populations, proactive “find and treat” services may be cost‐effective or even cost‐saving, as has been found for tuberculosis .…”

A Maximum Entropy Method for the Prediction of Size Distributions

“…In previous work [47,48], we have added further features to make the model more plausible for example, for epidemiology, such as clustering (that a link is preferably formed between neighbours of second or third degree), or different exit rules, for example, removal of a node after a given time span instead of exit by rate δ exit . The latter increases in addition the exponential cutoff, because it prevents nodes to remain a sufficiently long duration to attract many links.…”

Estimation and correction of bias in network simulations based on respondent-driven sampling data

“…Data collected by chain-referral recruiting methods, including RDS and others, is used to estimate network structure and parameters for simulation models of pathogen transmission across a sexual or injection drug use network since these sampling methods provide information on both the individual and pair-wise (dyadic) levels [12][13][14][15] . Some studies use the sample network as a proxy for the full network and simulate disease transmission on this sample network 12 ; others use information on network features from the sample network to reconstruct the whole network using statistical models such as exponential random graph models (ERGMs) or stochastic block model 13,15,16 .…”

“…Accurately capturing network structure is crucial, since it can play a significant role in disease transmission [16][17][18][19][20][21] . Three important features of network structure that impact disease transmission are: 1) density/mean degree, 2) homophily, and 3) transitivity 22,23 .…”