A model-based evaluation of the national immunization programme against rubella infection and congenital rubella syndrome in The Netherlands.

In order to improve the prevention of cases of congenital rubella syndrome in The Netherlands, in 1987 the selective vaccination strategy against rubella infection in girls was replaced by mass vaccination. This decision was supported by mathematical model analyses carried out by Van Druten and De Boo. In order to compare the predicted impact of the rubella vaccination programme with the current available data in more detail, a similar model was built. Although the model predicts elimination of the rubella virus, data show that virus circulation is still present at a higher level than expected by the model. Simulation studies indicate that import of infection and a lower vaccine effectiveness, related to possible asymptomatic reinfection of vaccinated people, could be sources contributing to the present virus circulation. Even though the number of infections is much higher than the number of reported cases of disease, limited serosurveillance data and case notification data show that females of childbearing age are well protected by immunization.

Kinetics of the IgG antibody response to pertussis toxin after infection with B. pertussis.

We aimed to provide a quantitative description of decay in pertussis antibody levels to aid in finding a serological estimate of the incidence of pertussis. The serum IgG response against pertussis toxin was studied in a group of clinically diagnosed patients. Individual records consisted of repeated serum IgG measurements at irregular intervals for up to 10 years post diagnosis. These data were analysed with a nonlinear regression model taking into account censoring at upper and lower threshold levels, measurement errors, and individual variation in the shape and magnitude of the immune response. There was considerable variation between individual responses, both in strength (amplitude) and duration (shape). The inverse model relating IgG levels to time from infection (diagnosis) can be applied to cross-sectional IgG data to generate distributions of times from infection, which may be used to calculate infection rates and their variation, in populations sampled for cross-sectional IgG data.