A new testing algorithm that accurately estimates the population-level incidence of hepatitis C virus infection may help improve the impact of public health efforts in high-risk populations.
A research team led by Oliver Laeyendecker, Ph.D., staff scientist at the National Institute of Allergy and Infectious Diseases (NIAID) and assistant professor of medicine and epidemiology at Johns Hopkins University, Baltimore, developed a hepatitis C virus (HCV) IgG antibody avidity assay by modifying the Ortho 3.0 HCV ELISA. Researchers tested 997 serum or plasma samples from 568 people who inject drugs (PWID) enrolled in prospective cohort studies. They discovered that, in various simulated high-risk populations, the multi-assay avidity-based testing algorithm had greater than 80% power to detect a 50% reduction in HCV incidence.
Courtesy NIH
“Application of an avidity-based testing algorithm to estimate [HCV] incidence from a cross-sectional survey can enhance current sentinel surveillance systems,” wrote Dr. Laeyendecker and his coauthors. “Among other emerging epidemics, the CDC has reported that HCV incidence is markedly on the rise among PWID in the U.S.
“However, in light of new information regarding the underreporting of newly acquired HCV cases to the CDC, these epidemics may be even more advanced than previously expected,” the researchers noted. “Individual acute HCV screening practices in combination with an avidity-based algorithm may collectively help to estimate incidence through practical sample sizes.”
Avidity-based testing algorithms have the capacity to quickly identify and confirm emerging HCV epidemics in many PWID populations, the investigators concluded.
Read the full study in the Journal of Infectious Diseases (J Infect Dis. 2016 Jan 14; doi: 10.1093/infdis/jiw005).
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