Performance of algorithms for identifying patients with chronic hepatitis B or C infection in the french health insurance claims databases using the ANRS CO22 HEPATHER cohort

Abstract

The validity of algorithms for identifying patients with chronic hepatitis B or C virus (HBV or HCV) infection in claims databases has been little explored. The performance of 15 algorithms was evaluated. Data from HBV- or HCV-infected patients enrolled between August 2012 and December 2015 in French hepatology centres (ANRS CO22 HEPATHER cohort) were individually linked to the French national health insurance system (SNDS). The SNDS covers 99% of the French population and contains healthcare reimbursement data. Performance metrics were calculated by comparing the viral status established by clinicians with those obtained with the algorithms identifying chronic HBV- and HCV-infected patients. A total of 14 751 patients (29% with chronic HBV and 63% with chronic HCV infection) followed-up until December 2018 were selected. Despite good specificity, the algorithms relying on ICD-10 codes performed poorly. By contrast, the multi-criteria algorithms combining ICD-10 codes, antiviral dispensing, laboratory diagnostic tests (HBV DNA or HCV RNA detection and quantification, HCV genotyping), examinations for the assessment of liver fibrosis and long-term disease registrations were the most effective (sensitivity 0.92, 95% CI, 0.91-0.93 and specificity 0.96, 95% CI, 0.95-0.96 for identifying chronic HBV-infected patients; sensitivity 0.94, 95% CI, 0.94-0.94 and specificity 0.85, 95% CI, 0.84-0.86 for identifying chronic HCV-infected patients). In conclusion, the multi-criteria algorithms perform well in identifying patients with chronic hepatitis B or C infection and can be used to estimate the magnitude of the public health burden associated with hepatitis B and C in France.

Keywords: hepatitis B; hepatitis C; routinely collected health data; sensitivity and specificity; validation study.