In this paper, we report an overall HCV antibody prevalence of 31% for a sample of young people ages 18–29 who injected drugs (PWID), recruited between 2014–2016 in NYC. This HCV prevalence is lower than the 42% in Lower East Side and 51% in Harlem reported by Diaz et al. among similar samples of street-recruited PWID ages 18–29 between 1997–1998 (43). The lower prevalence reported in this manuscript might indicate a decrease in the overall prevalence among young PWID in NYC. This possible overall trend is also presented in the NYC Hepatitis A, B, and C annual report, 2018, which indicates a 22% reduction in the number of HCV infections among those 29 or younger during the period 2009–2018 (44). The overall HCV incidence among the study sample was 10 per 100 person-years. This cross-sectional incidence was lower than 18 per 100 person-years reported by the DUIT prospective study (2002–2004), among a street recruited NYC sample of 18-30-year-old PWID and 19.5 per 100 person-years among a drug treatment sample of older PWID (mean age 41.2) recruited between 2006–2013 (29). These data suggest that a reduction in HCV prevalence and incidence among young PWID in NYC is perhaps the result of long sustained harm reduction efforts (needle exchange programs, medically assisted treatment, and HCV treatment) (45). It is worth noting that the overall prevalence for young PWID is less than half of the 67% HCV prevalence reported for older PWID during the 2006–2013 period (29).
Variables significantly associated with HCV Ab + in the present analysis are similar to those reported by other studies. HCV positive status was independently correlated with drug injection-related variables such as sharing cookers with two or more people and injecting drugs for 4 or more years consistent with multiple studies indicating the risk of sharing injection paraphernalia and length of drug injection period (11, 13–15, 46). HCV positive status was also independently correlated with structural factors such as lifetime homelessness and being incarcerated two or more times consistent with other studies reporting similar HCV risks (13, 14, 21, 43, 47). In conjunction, these results may indicate an overall positive impact of harm reduction efforts in reducing HCV prevalence among young PWID in NYC while injection risks and structural factors remain areas of key concern. If we are to reach HCV elimination among youth, both injection risk and structural factors will need to be addressed at the same time.
In an effort to better understand how these risk factors interact and what policies could be effective in reducing HCV infections, we resorted to system dynamics (SD) modeling. SD modeling is a systems science methodology that is well suited for population studies where multiple feedback effects, time delays, and nonlinearities are taken into account. SD has been increasingly applied to public health problems including chronic disease (48), epidemics (49), human immunodeficiency virus (HIV) (50), and drug abuse (51). Recent literature has applied SD to explore the impact of potential policy changes, including changes in opioid prescription dosage, drug diversion, OUD treatment, and naloxone distribution on opioid-related outcomes (52–54). However, to our knowledge, no studies thus far have adopted SD modeling to better understand the spread of HCV among young opioid users, despite the interdependence and non-linearity of multiple underlying factors.
In this manuscript, we provide an example of how epidemiologic analysis and statistical associations can be the foundation for the development of SD modeling to better understand how multiple factors of different order (e.g. structural and injection risk) interact to increase rates of HCV infection among young PWID. SD is able to take basic epidemiological findings and create a visual model of how the interactions among variables could have a multiplicative effect in increasing HCV transmission. In this paper, we illustrate how the dynamic interactions among variables interact to increase the risk of HCV among young opioid users. We developed an SD model that depicts the interaction among structural factors (e.g. drug treatment, HCV treatment, harm reduction, criminal justice, and homelessness), injection networks (knowing opioid users older than 29), and injection trajectories among young adults (e.g. transition to drug injection and number of years of drug injection). Also, to further explore the interplay of these system components leading to HCV, we introduce the possible impact of potential policy changes affecting these key factors (e.g., housing assistance, HCV treatment, reducing incarceration).
For example, as more young PWID who are HCV-infected receive HCV treatment, an increasing number of them will clear the virus and flow back into the stock of ‘HCV Susceptible – Young PWID’ with less or equal to 1 year or multiple years of drug injection (Fig. 2). Thus, the HCV prevalence decreases, and the R1 – HCV Spread among Young PWID with < 1 Year of drug injection and R2 – HCV Spread among Young PWID with > 1 Year of drug injection feedback loops become virtuous and slow the spread of HCV at an increasing rate. However, as young PWID who are HCV positive continue to inject drugs, the baseline HCV prevalence within young injection networks increases, leading to a higher likelihood of infection per injection risk event among the uninfected. Consequently, the feedback loops R1 and R2, lead to an increasingly rapid spread of HCV among PWID. The feedback loop R2 has even a stronger impact on the spread of HCV due to the much higher HCV incidence among Young PWID with > 1 year IDU, indicating the need to prevent recently initiated injectors from becoming long-term injectors, further preventing the spread of HCV. Prevention efforts and HCV treatment focusing on recently initiated PWID could help prevent and eventually eliminate HCV among young PWID (34, 38).
Homelessness is another risk factor that has been shown to be associated with greater vulnerability to HCV infection for a variety of reasons, such as the increased likelihood of injecting in public spaces and limited ability to store sterile injection equipment (23–25). Thus, homelessness can make the reinforcing loops R1 and R2 become even more vicious and exponentially spread HCV. Since individuals who inject drugs for multiple years may be at increased risk of homelessness due to loss of social support and the economic burden of sustaining their drug use, there is an even further urgency to provide housing and prevent transition and continuation of injection drug use (e.g., by expanding and facilitating access to evidence-based drug treatment). As young PWID initiate drug treatment and flow out of the stock of ‘HCV Susceptible’ with less than 1 year or multiple years of drug injection, they are less vulnerable to becoming HCV infected, which will slow down the spread of HCV by reducing the number of individuals who could potentially be exposed.
The model also includes the variable ‘knowing opioid users older than 29’ and its impact on the spread of HCV among young PWID (Fig. 2). As we presented earlier, the large difference in prevalence among older and younger samples might be due to the tendency of young PWID to interact with drug users similar of age (38). This partial separation between older and younger PWID and reducing the chances of ‘knowing opioid users older than 29’ could serve as a partial barrier to the spread of HCV from groups of older PWID who have higher HCV prevalence rates. Additionally, incarceration increases the likelihood of HCV infection according to our analysis and SD model. Other research also indicates the high prevalence of HCV among incarcerated populations (20–22).
In our SD model (Fig. 2) we have illustrated some potential leverage points by introducing policies (e.g. housing for PWID, reducing incarceration, harm reduction services, separation between older and younger PWID) that could counteract the multiplying effect of key variables (e.g. homelessness, incarceration, paraphernalia sharing). For clarity, in the model (Fig. 2), the text presenting policy/intervention leverage points is underlined and bolded. For example, by expanding harm reduction services such as syringe service programs, the risk of exposure to HCV through repeated sharing of needles and cookers could be reduced. Furthermore, in order to break the loop of contagion, strategies and interventions could focus on preventing young opioid users from transitioning to injection drug use by providing medication for opioid use disorder (MOUD) before they begin to inject drugs or early in their injection careers. Additionally, treating HCV-infected young PWID (causing them to exit the model’s two lower stocks) could decrease the baseline HCV prevalence in the young PWID population, thereby reducing the spread of HCV. Furthermore, HCV treatment, if initiated in a timely manner, could assist with HCV elimination within injection networks (35).
A system dynamics approach extends our ability to study a complex problem from a linear traditional data analysis approach to a non-linear and operational thinking methodology, which is substantially needed to tackle the complexity of the HCV epidemic more effectively (32). SD modeling helps us to better understand interactions between variables, highlighting structural components beyond individual behaviors, and facilitates designing comprehensive prevention policies that would include addressing such structural factors. The SD model presented in this paper serves as the basis for developing a simulation model that captures the spread of HCV among young opioid users by mathematically quantifying the links. Once the model is validated towards historical time series data, we can then use the model to test what-if simulation scenarios and evaluate the effectiveness, sustainability, and unintended consequences of the aforementioned suggested intervention and policy strategies. In future research, this SD model may facilitate the generation of novel hypotheses and in silico evaluation of the combined effects of various intervention strategies over the short and long term, as well as the identification of potential unintended consequences of alternative interventions.
In our novel approach for this manuscript, we illustrate how SD modeling can facilitate the integration of different methodologies in social scientific and public health research: it graphically represents statistical findings while allowing researchers to incorporate their qualitative knowledge of how these variables interact. It also allows researchers to graphically represent areas of prevention and identify leverage points within the system where policy efforts could be most effective.
This study has some limitations. First, given that it is a cross-sectional study, our data only provides a snapshot of the prevalence of HCV status among young opioid users in NYC and cannot establish causation. Second, this study focuses exclusively on young adults in NYC who inject drugs. The results, therefore, may not be generalizable to drug users of other ages or in other areas, particularly those residing in non-urban areas. Third, the use of a non-random recruitment strategy – Respondent-Driven Sampling – may have also introduced bias into the sample that limits the generalizability of the findings. Lastly, the participants’ ability to recall past exposures makes this study susceptible to recall bias.
Our results suggest recommendations for further research and intervention strategies. Further research should investigate the relationship between HCV positive status and incarceration among young opioid users. Interventions that target the homeless population and those involved with criminal justice may also be an efficient way to identify young PWID at risk for HCV and treat those who are HCV-positive. Since young uninfected PWID are connected to young opioid users who are infected, these connections also provide a pathway for the transmission of HCV. Therefore, harm reduction efforts should teach young injectors skills and strategies to enable long-term risk avoidance and the implementation of healthy protective behaviors among injectors and their networks.
To conclude, young PWID are at considerable risk for HCV and thus, a key population for intervention. Despite the study’s limitations, the current findings suggest that harm reduction services should make concerted efforts to reach young PWID with histories of incarceration and/or homelessness, and networks with older PWID.