Identifying immunity gaps for measles using Belgian serial serology data

Abstract

Vaccine-preventable diseases, such as measles, have been re-emerging in countries with moderate to high vaccine uptake. It is increasingly important to identify and close immunity gaps and increase coverage of routine childhood vaccinations, including two doses of the measles-mumps-rubella vaccine (MMR). Here, we present a simple cohort model relying on a Bayesian approach to evaluate the evolution of measles seroprevalence in Belgium using the three most recent cross-sectional serological survey data collections (2002, 2006 and 2013) and information regarding vaccine properties. We find measles seroprevalence profiles to be similar for the different regions in Belgium. These profiles exhibit a drop in seroprevalence in birth cohorts that were offered vaccination at suboptimal coverages in the first years after routine vaccination has been started up. This immunity gap is observed across all cross-sectional survey years, although it is more pronounced in survey year 2013. At present, the COVID-19 pandemic could negatively impact the immunization coverage worldwide, thereby increasing the need for additional immunization programs in groups of children that are impacted by this. Therefore, it is now even more important to identify existing immunity gaps and to sustain and reach vaccine-derived measles immunity goals.

Keywords: Bayesian MCMC; Immunity goals; Measles elimination; Serial serological survey data.

Fig. 1

Panel 1: Lexis diagram showing the aging of (Belgian) birth cohorts born at different calendar times (gray oblique lines, one line per birth cohort), together with the timing of the serial serological survey data collections (in 2002, 2006 and 2013; black dashed vertical lines) and timing of MMR vaccination (horizontal blue and red lines). Panel 2–4: Observed measles seroprevalence (with 95% Clopper-Pearson confidence limits) for Belgium in 2002, 2006 and 2013 based on donor or residual samples. The age distribution of the donor and residual samples are displayed in blue and red respectively in panel 2–4. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Estimated measles seroprevalence by birth cohort (year) for Flanders and Wallonia & Brussels Capital Region based on three Belgian serial serological survey data collections in 2002, 2006 and 2013 using the Bayesian cohort model.

Fig. 3

Cohort-specific estimated exposure rates for Flanders (red) and Wallonia & Brussels Capital Region (blue) and in gray their pointwise 95% credible intervals, with standardized yearly incidence (bars) of reported measles cases per 100 susceptible individuals as reported between 2003 and 2013 by birth cohort (for the whole of Belgium). By standardizing, differences in vaccination coverage (in different years) and the effect of primary and secondary vaccine failure are accounted for. The reported cases by birth cohort are divided by the total number of susceptible individuals. Reported cases were extracted from the ECDC Surveillance Atlas of Infectious Diseases [23]. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)