Estimation of Infection Force of Hepatitis C Virus Among Drug Users in France

The spread of diseases is a dynamic and complex phenomenon. In the world, they are due to misery and poverty. To understand epidemiological systems is essential for governments. Since modeling simplifies reality, it is an excellent method. The hepatitis C virus (HCV) infection is common worldwide, and injection drug use remains the major mode of transmission of the disease, especially because of equipment sharing. Consequently, it is crucial to monitor the HCV transmission dynamics over time and to assess the effect of harm reduction measures. The aim of this work is to estimate the force of infection of hepatitis C from two national cross-sectional epidemiological surveys conducted in 2004 and 2011 by the French Institute for Public Health Surveillance and its partners in a drug user population in France. HCV prevalence was estimated according to age and calendar time through fractional polynomials adjusted or not to the HIV serological status and to injected drug users or oral drug users in general. The force of infection was modeled according to an SIS (susceptible–infected–susceptible) compartmental model using ordinary differential equations (ODE) and as a function of the derivative of the prevalence function depending on age, time, HIV serological status, and having injected at least once in their life, from 2000 to 2020. Our model was applied on real and simulated surveys using R and Stata software. The results show that HCV prevalence and the force of infection are linked to age and time, and are very high for drug users who injected at least once in their life and who are simultaneously HCV and HIV infected. Based on this model, we estimated that HCV incidence will continue to decline over the following years. Currently in France, there is no cohort study of the HCV among drug users. The only way to estimate HCV incidence in the French population is to use the only existing two national cross-sectional surveys. Our work provides guidance for researchers to compare several cross-sectional epidemiological surveys among drug users and proposes an alternative method to estimate the force of infection among drug users from cross-sectional surveys in the absence of a cohort.