Eric Tatara

Background: People who inject drugs (PWIDs) are at high risk for acquiring and transmitting hepatitis C virus (HCV). Direct-acting antiviral (DAA) therapy leads to high cure rates. However, lack of protective immunity after cure and high re-infection rates in PWID necessitates access to multiple DAA treatments per PWID to reach the World Health Organization HCV elimination goal of 90% incidence reduction. A major public health challenge is determining the proportion of PWID to treat to reach the goal and the continued treatment levels required to maintain decreased incidence. Method: We used an agent-based model to study varying levels of DAA treatment among HCV-infected PWID from Chicago, IL, and the effects of reducing DAA treatment after elimination is reached. The model uses individual temporal viral load profiles to determine transmission probabilities relative to the HCV RNA titers of receptive syringe-sharing PWID. Results: Modeling predicts that insufficient DAA treatment can result in a continuous increase of HCV incidence over a 25-year period, with no significant decrease. Elimination can be achieved within 10 years with sufficient treatment levels. However, when DAA treatment is stopped, rates of new chronic HCV infections rapidly increase, returning to preelimination levels within 5 years. Annual treatment of ≥0.5% would maintain the elimination goal, but identifying and treating small numbers of infected people annually in PWID populations would be resource intensive. Conclusions: The challenge to achieve and maintain the elimination goal underscores the importance of augmenting DAA treatment with intervention strategies such as syringe service programs and vaccines.

Global elimination of chronic hepatitis C (CHC) remains difficult without an effective vaccine. Since injection drug use is the leading cause of hepatitis C virus (HCV) transmission in Western Europe and North America, people who inject drugs (PWID) are an important population for testing HCV vaccine effectiveness in randomized-clinical trials (RCTs). However, RCTs in PWID are inherently challenging. To accelerate vaccine development, controlled human infection (CHI) models have been suggested as a means to identify effective vaccines. To bridge the gap between CHI models and real-world testing, we developed an agent-based model simulating a two-dose vaccine to prevent CHC in PWID, representing 32,000 PWID in metropolitan Chicago and accounting for networks and HCV infections. We ran 500 trial simulations under 50 and 75% assumed vaccine efficacy (aVE) and sampled HCV infection status of recruited in silico PWID. The mean estimated vaccine efficacy (eVE) for 50 and 75% aVE was 48% (SD ± 12) and 72% (SD ± 11), respectively. For both conditions, the majority of trials (∼71%) resulted in eVEs within 1 SD of the mean, demonstrating a robust trial design. Trials that resulted in eVEs >1 SD from the mean (lowest eVEs of 3 and 35% for 50 and 75% aVE, respectively), were more likely to have imbalances in acute infection rates across trial arms. Modeling indicates robust trial design and high success rates of finding vaccines to be effective in real-life trials in PWID. However, with less effective vaccines (aVEs∼50%) there remains a higher risk of concluding poor vaccine efficacy due to post-randomization imbalances.

Access to treatment and medication for opioid use disorder (MOUD) is essential in reducing opioid use and associated behavioral risks, such as syringe sharing among persons who inject drugs (PWID). Syringe sharing among PWID carries high risk of transmission of serious infections such as hepatitis C and HIV. MOUD resources, such as methadone provider clinics, however, are often unavailable to PWID due to barriers like long travel distance to the nearest methadone provider and the required frequency of clinic visits. The goal of this study is to examine the uncertainty in the effects of travel distance in initiating and continuing methadone treatment and how these interact with different spatial distributions of methadone providers to impact co-injection (syringe sharing) risks. A baseline scenario of spatial access was established using the existing locations of methadone providers in a geographical area of metropolitan Chicago, Illinois, USA. Next, different counterfactual scenarios redistributed the locations of methadone providers in this geographic area according to the densities of both the general adult population and according to the PWID population per zip code. We define different reasonable methadone access assumptions as the combinations of short, medium, and long travel distance preferences combined with three urban/suburban travel distance preference. Our modeling results show that when there is a low travel distance preference for accessing methadone providers, distributing providers near areas that have the greatest need (defined by density of PWID) is best at reducing syringe sharing behaviors. However, this strategy also decreases access across suburban locales, posing even greater difficulty in regions with fewer transit options and providers. As such, without an adequate number of providers to give equitable coverage across the region, spatial distribution cannot be optimized to provide equitable access to all PWID. Our study has important implications for increasing interest in methadone as a resurgent treatment for MOUD in the United States and for guiding policy toward improving access to MOUD among PWID.

Despite the availability of direct-acting antivirals that cure individuals infected with the hepatitis C virus (HCV), developing a vaccine is critically needed in achieving HCV elimination. HCV vaccine trials have been performed in populations with high incidence of new HCV infection such as people who inject drugs (PWID). Developing strategies of optimal recruitment of PWID for HCV vaccine trials could reduce sample size, follow-up costs and disparities in enrollment. We investigate trial recruitment informed by machine learning and evaluate a strategy for HCV vaccine trials termed PREDICTEE—Predictive Recruitment and Enrichment method balancing Demographics and Incidence for Clinical Trial Equity and Efficiency. PREDICTEE utilizes a survival analysis model applied to trial candidates, considering their demographic and injection characteristics to predict the candidate’s probability of HCV infection during the trial. The decision to recruit considers both the candidate’s predicted incidence and demographic characteristics such as age, sex, and race. We evaluated PREDICTEE using in silico methods, in which we first generated a synthetic candidate pool and their respective HCV infection events using HepCEP, a validated agent-based simulation model of HCV transmission among PWID in metropolitan Chicago. We then compared PREDICTEE to conventional recruitment of high-risk PWID who share drugs or injection equipment in terms of sample size and recruitment equity, with the latter measured by participation-to-prevalence ratio (PPR) across age, sex, and race. Comparing conventional recruitment to PREDICTEE found a reduction in sample size from 802 (95%: 642–1010) to 278 (95%: 264–294) with PREDICTEE, while also reducing screening requirements by 30%. Simultaneously, PPR increased from 0.475 (95%: 0.356–0.568) to 0.754 (95%: 0.685–0.834). Even when targeting a dissimilar maximally balanced population in which achieving recruitment equity would be more difficult, PREDICTEE is able to reduce sample size from 802 (95%: 642–1010) to 304 (95%: 288–322) while improving PPR to 0.807 (95%: 0.792–0.821). PREDICTEE presents a promising strategy for HCV clinical trial recruitment, achieving sample size reduction while improving recruitment equity.

Background Access to treatment and medication for opioid use disorder (MOUD), such as methadone, is essential for improving health outcomes by reducing infection and overdose risks associated with injection drug use. MOUD resource distribution, however, is often a complex interplay of social and structural factors that result in nuanced patterns reflecting underlying social and spatial inequities. Persons who inject drugs (PWID) that receive MOUD treatment experience a reduction in the number of daily drug injections and a reduction in the number of syringe sharing episodes with other individuals. We assessed the impact on reduction in syringe sharing behaviors among PWID who are adherent to methadone treatment via simulation studies. Methods Actual (real-world) and counterfactual scenarios of varying levels of social and spatial inequity to providers of methadone were evaluated using HepCEP, a validated agent-based model of syringe sharing behaviors among people who inject drugs (PWID) in metropolitan Chicago, Illinois, U.S.A. Synthetic spatial distributions reflecting disparate geographic patterns of methadone provider location and population characteristics are evaluated to show how population-level health outcomes vary accordingly. Results For all methadone access assumptions and provider location distributions, redistributing methadone providers results in some areas with poor access to MOUDs. All scenarios exhibited some areas with poor access, highlighting the scarcity of providers in the region as a major challenge. Need-based distributions are more like the actual provider distribution, indicating that the actual spatial distribution of methadone providers already reflects the local need for MOUD resources. Conclusions The impact of the spatial distribution of methadone providers on syringe sharing frequency is dependent on access. When there are significant structural barriers to accessing methadone providers, distributing providers near areas that have the greatest need (as defined by density of PWID) is optimal.

Hepatitis C virus (HCV) infection is a leading cause of chronic liver disease and mortality worldwide. Direct-acting antiviral (DAA) therapy leads to high cure rates. However, persons who inject drugs (PWID) are at risk for reinfection after cure and may require multiple DAA treatments to reach the World Health Organization’s (WHO) goal of HCV elimination by 2030. Using an agent-based model (ABM) that accounts for the complex interplay of demographic factors, risk behaviors, social networks, and geographic location for HCV transmission among PWID, we examined the combination(s) of DAA enrollment (2.5%, 5%, 7.5%, 10%), adherence (60%, 70%, 80%, 90%) and frequency of DAA treatment courses needed to achieve the WHO’s goal of reducing incident chronic infections by 90% by 2030 among a large population of PWID from Chicago, IL and surrounding suburbs. We also estimated the economic DAA costs associated with each scenario. Our results indicate that a DAA treatment rate of >7.5% per year with 90% adherence results in 75% of enrolled PWID requiring only a single DAA course; however 19% would require 2 courses, 5%, 3 courses and <2%, 4 courses, with an overall DAA cost of $325 million to achieve the WHO goal in metropolitan Chicago. We estimate a 28% increase in the overall DAA cost under low adherence (70%) compared to high adherence (90%). Our modeling results have important public health implications for HCV elimination among U.S. PWID. Using a range of feasible treatment enrollment and adherence rates, we report robust findings supporting the need to address re-exposure and reinfection among PWID to reduce HCV incidence.

Criminal justice involved (CJI) individuals with a history of opioid use disorder (OUD) are at high risk of overdose and death in the weeks following release from jail. We developed the Justice-Community Circulation Model (JCCM) to investigate OUD-CJI dynamics post-release and the effects of interventions on overdose deaths. The JCCM uses a synthetic agent-based model population of approximately 150,000 unique individuals that is generated using demographic information collected from multiple Chicago-area studies and data sets. We use a high-performance computing (HPC) workflow to implement a sequential approximate Bayesian computation algorithm for calibrating the JCCM. The calibration results in the simulated joint posterior distribution of the JCCM input parameters. The calibrated model is used to investigate the effects of a naloxone intervention for a mass jail release. The simulation results show the degree to which a targeted intervention focusing on recently released jail inmates can help reduce the risk of death from opioid overdose.

Hepatitis C virus (HCV) infection is a leading cause of chronic liver disease and mortality worldwide. Direct-acting antiviral (DAA) therapy leads to high cure rates. However, persons who inject drugs (PWID) are at risk for reinfection after cure and may require multiple DAA treatments to reach the World Health Organization’s (WHO) goal of HCV elimination by 2030. Using an agent-based model (ABM) that accounts for the complex interplay of demographic factors, risk behaviors, social networks, and geographic location for HCV transmission among PWID, we examined the combination(s) of DAA enrollment (2.5%, 5%, 7.5%, 10%), adherence (60%, 70%, 80%, 90%) and frequency of DAA treatment courses needed to achieve the WHO’s goal of reducing incident chronic infections by 90% by 2030 among a large population of PWID from Chicago, IL and surrounding suburbs. We also estimated the economic DAA costs associated with each scenario. Our results indicate that a DAA treatment rate of >7.5% per year with 90% adherence results in 75% of enrolled PWID requiring only a single DAA course; however19% would require 2 courses, 5%, 3 courses and <2%, 4 courses), with an overall DAA cost of $325 million to achieve the WHO goal in metropolitan Chicago. We estimate a 28% increase in the overall DAA cost under low adherence (70%) compared to high adherence (90%). Our modeling results have important public health implications for HCV elimination among U.S. PWID. Using a range of feasible treatment enrollment and adherence rates, we report robust findings supporting the need to address re-exposure and reinfection among PWID to reduce HCV incidence.