Table 2:

Summary of using alternative indicators for monitoring decreases in hepatitis C virus (HCV) incidence

Alternative indicator	Relationship with HCV incidence	Factors that can affect relationship with HCV incidence	Minimum country-level data needed and their data sources (only included for useable indicators 1 and 3)
1. Trends in chronic HCV prevalence	Tracks trends in HCV incidence well in different settings and populations	• Prevention intervention scale-up and other factors directly affecting HCV incidence • Population heterogeneity in risk and targeting of HCV treatment Note: numerous other factors did not affect the relationship*	Trends in chronic HCV prevalence at baseline and endpoint of HCV elimination initiative for all population groups contributing significantly to HCV transmission Data Sources Gold standard for general population is population-based surveys such as the Demographic Health Survey (DHS) or the HIV Impact Assessment surveys (PHIA). Other routine testing data, such as among pregnant women or blood donors, may be useable but likely to have biases. Gold standard for populations at high risk of infection (e.g., PWID, MSM, prisoners) are national bio-behavioural surveys (already done for HIV in many settings). Routine testing within services or institutions such as harm reduction programs, sexual health clinics or prisons may be useable but likely to have biases.
2. Trends in HCV antibody prevalence	Does not track HCV incidence well: relationship is highly variable, even among young or recent PWID	• Prevention intervention scale-up • Population turnover • Population heterogeneity in risk and targeting of HCV treatment	N/A
3. Scale-up levels of HCV interventions	Tracks trends in HCV incidence well but no universal target can be set; country-specific modelling is needed	• Prevention intervention scale-up • Population growth • Underlying epidemic dynamics • Elimination time frame • Population heterogeneity in risk and targeting of HCV treatment	The following data are needed for all population groups that are contributing significantly to HCV transmission: • Baseline chronic or antibody HCV prevalence and historic trends in prevalence Data Sources: similar as for #1 • Scale-up levels of HCV treatment and HCV preventative interventions Data Sources: administrative data (e.g., health records, prescription registries), programmatic data (e.g., data collected as part of harm reduction programs or community clinics) and self-reported data captured through population-based surveys • The effect of HCV prevention interventions in reducing HCV risk Data Sources: country-specific studies or systematic reviews, such as for estimating the effectiveness of OST and NSP in PWID[82]; evidence is limited for community-level interventions and so may need to rely on indirect data or conservatively discount their effect altogether • Population size for different risk groups Data Sources: country-specific size estimation studies • Rate of population growth Data Sources: projections by international agencies
4. Scale-up levels of HCV testing	Does not track HCV incidence well: relationship is highly variable	• Prevention intervention scale-up • Population sub-groups that are tested and retested • Downstream cascade of care (e.g., referral for care, uptake of HCV treatment)	N/A

Abbreviations: N/A = not applicable; NSP = needle and syringe program; OST = opioid substitution treatment

^*The models considering the chronic HCV prevalence indicator also varied in terms of their baseline prevalence of chronic HCV infection, risk group modelled, underlying dynamics of the epidemic (stable or increasing), levels of population growth (stable or growing), and time period over which elimination was achieved. None of these factors seemed to affect the relationship between chronic prevalence and incidence.