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Register a new userDynamic modelling of hepatitis C virus transmission among people who inject drugs: a methodological review
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Anthony Cousien
Publication date
2013
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Mathematical Statistics
and research's language is
English
Authors
Anthony Cousien
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Equipment sharing among people who inject drugs (PWID) is a key risk factor in infection by hepatitis C virus (HCV). Both the effectiveness and cost-effectiveness of interventions aimed at reducing HCV transmission in this population (such as opioid substitution therapy, needle exchange programs or improved treatment) are difficult to evaluate using field surveys. Ethical issues and complicated access to the PWID population make it difficult to gather epidemiological data. In this context, mathematical modelling of HCV transmission is a useful alternative for comparing the cost and effectiveness of various interventions. Several models have been developed in the past few years. They are often based on strong hypotheses concerning the population structure. This review presents compartmental and individual-based models in order to underline their strengths and limits in the context of HCV infection among PWID. The final section discusses the main results of the papers.
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Background: Highly effective direct-acting antiviral (DAA) regimens (90% efficacy) are becoming available for hepatitis C virus (HCV) treatment. This therapeutic revolution leads us to consider possibility of eradicating the virus. However, for this, an effective cascade of care is required. Methods: In the context of the incoming DAAs, we used a dynamic individual-based model including a model of the people who inject drugs (PWID) social network to simulate the impact of improved testing, linkage to care, and adherence to treatment, and of modified treatment recommendation on the transmission and on the morbidity of HCV in PWID in France. Results: Under the current incidence and cascade of care, with treatment initiated at fibrosis stage $ge$F2, the HCV prevalence decreased from 42.8% to 24.9% [95% confidence interval 24.8%--24.9%] after 10 years. Changing treatment initiation criteria to treat from F0 was the only intervention leading to a substantial additional decrease in the prevalence, which fell to 11.6% [11.6%--11.7%] at 10 years. Combining this change with improved testing, linkage to care, and adherence to treatment decreased HCV prevalence to 7% [7%--7.1%] at 10 years and avoided 15.3% [14.0%-16.6%] and 29.0% [27.9%--30.1%] of cirrhosis complications over 10 and 40 years respectively. Conclusion: A high decrease in viral transmission occurs only when treatment is initiated before liver disease progresses to severe stages, suggesting that systematic treatment in PWID, where incidence remains high, would be beneficial. However, eradication will be difficult to achieve.
Social context plays an important role in perpetuating or reducing HIV risk behaviors. This study analyzed the network and individual attributes that were associated with the likelihood that people who inject drugs (PWID) will engage in HIV risk behaviors with one another. We analyze data collected in the Social Risk Factors and HIV Risk Study (SFHR) and Transmission Reduction Intervention Project (TRIP) to perform the analysis. Exponential random graph models were used to determine which attributes were associated with the likelihood of people engaging in HIV risk behaviors, such as injection behaviors that are associated with one another, among PWID. Results across all models and across both data sets indicated that people were more likely to engage in risk behaviors with others who were similar to them in some way (e.g., were the same sex, race/ethnicity, living conditions). In both SFHR and TRIP, we explore the effects of missingness at individual and network levels on the likelihood of individuals to engage in HIV risk behaviors among PWID. In this study, we found that known individual-level risk factors, including housing instability and race/ethnicity, are also important factors in determining the structure of the observed network among PWID. Future development of interventions should consider not only individual risk factors, but communities and social influences leaving individuals vulnerable to HIV risk.
Evaluating causal effects in the presence of interference is challenging in network-based studies of hard to reach populations. Like many such populations, people who inject drugs (PWID) are embedded in social networks and often exert influence on others in their network. In our setting, the study design is observational with a non-randomized network-based HIV prevention intervention. The information is available on each participant and their connections that confer possible shared HIV risk behaviors through injection and sexual risk behaviors. We consider two inverse probability weighted (IPW) estimators to quantify the population-level effects of non-randomized interventions on subsequent health outcomes. We demonstrated that these two IPW estimators are consistent, asymptotically normal, and derived a closed form estimator for the asymptotic variance, while allowing for overlapping interference sets (groups of individuals in which the interference is assumed possible). A simulation study was conducted to evaluate the finite-sample performance of the estimators. We analyzed data from the Transmission Reduction Intervention Project, which ascertained a network of PWID and their contacts in Athens, Greece, from 2013 to 2015. We evaluated the effects of community alerts on HIV risk behavior in this observed network, where the links between participants were defined by using substances or having unprotected sex together. In the study, community alerts were distributed to inform people of recent HIV infections among individuals in close proximity in the observed network. The estimates of the risk differences for both IPW estimators demonstrated a protective effect. The results suggest that HIV risk behavior can be mitigated by exposure to a community alert when an increased risk of HIV is detected in the network.
This study demonstrates the implementation of the stochastic ruler discrete simulation optimization method for calibrating an agent-based model (ABM) developed to simulate hepatitis C virus (HCV) transmission. The ABM simulates HCV transmission between agents interacting in multiple environments relevant for HCV transmission in the Indian context. Key outcomes of the ABM are HCV and injecting drug user (IDU) prevalences among the simulated cohort. Certain input parameters of the ABM need to be calibrated so that simulation outcomes attain values as close as possible to real-world HCV and IDU prevalences. We conceptualize the calibration process as a discrete simulation optimization problem by discretizing the calibration parameter ranges, defining an appropriate objective function, and then applying the stochastic ruler random search method to solve this problem. We also present a method that exploits the monotonic relationship between the simulation outcomes and calibration parameters to yield improved calibration solutions with lesser computational effort.
Chaos and oscillations continue to capture the interest of both the scientific and public domains. Yet despite the importance of these qualitative features, most attempts at constructing mathematical models of such phenomena have taken an indirect, quantitative approach, e.g. by fitting models to a finite number of data-points. Here we develop a qualitative inference framework that allows us to both reverse engineer and design systems exhibiting these and other dynamical behaviours by directly specifying the desired characteristics of the underlying dynamical attractor. This change in perspective from quantitative to qualitative dynamics, provides fundamental and new insights into the properties of dynamical systems.
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