Background: The current French hepatitis C virus infection screening programme is not yet reaching all populations at risk. In order to better identify individuals that would benefit from a screening test, we investigated an expanded combination of personal characteristics as potential screening criteria for this infection.
Methods: We constructed two multiple-regression models predicting hepatitis C antibody seropositivity using the population sample from the 2004 French national hepatitis C antibody seroprevalence survey (SPS) (n = 14 416): one representing current screening guidelines and another constructed from personal characteristics collected for the SPS. Performance of the two predictive models was statistically compared and we internally validated the better performing model.
Results: The expanded screening criteria model better discriminated seropositive and seronegative individuals [area under the ROC curve (AUC) 0.869 (95% CI 0.861-0.873)] than the current screening guidelines model [AUC 0.821 (95% CI 0.810-0.824)]. This performance difference was statistically significant (P < 0.00001). The expanded criteria model contains the variables age, sex, pre-1992 blood transfusion, intra-venous drug use, receipt of medical welfare for precarious individuals, previous surgeries, illicit nasal drug use, previous hepatitis C screening, tattoo, raised alanine aminotransferase level and birth in a hepatitis C high/moderate-prevalence country.
Conclusion: Results indicate that an expanded combination of screening criteria better predicted hepatitis C antibody status and thus individuals needing screening than the current French-screening guidelines. The proposed combination of screening criteria could more effectively target hepatitis C risk-populations in France and could serve as the basis for a decision-making screening tool for the general population.