Defining the Recipe for an Optimal Rotavirus Vaccine Introduction in a High-Income Country in Europe

Abstract

Observational data over 15 years of rotavirus vaccine introduction in Belgium have indicated that rotavirus hospitalisations in children aged <5 years plateaued at a higher level than expected, and was followed by biennial disease peaks. The research objective was to identify factors influencing these real-world vaccine impact data. We constructed mathematical models simulating rotavirus-related hospitalisations by age group and year for those children. Two periods were defined using different model constructs. First, the vaccine uptake period encompassed the years required to cover the whole at-risk population. Second, the post-uptake period covered the years in which a new infection/disease equilibrium was reached. The models were fitted to the observational data using optimisation programmes with regression and differential equations. Modifying parameter values identified factors affecting the pattern of hospitalisations. Results indicated that starting vaccination well before the peak disease season in the first year and rapidly achieving high coverage was critical in maximising early herd effect and minimising secondary sources of infection. This, in turn, would maximise the reduction in hospitalisations and minimise the size and frequency of subsequent disease peaks. The analysis and results identified key elements to consider for countries initiating an optimal rotavirus vaccine launch programme.

Measles, mumps, and rubella vaccine at age 6 months and hospitalisation for infection before age 12 months: randomised controlled trial

“…To account for known potential confounders 42 43 44 45 46 47 48 49 50 and to increase power, we performed an analysis adjusted for all baseline characteristics. We did not include the measles immunisation status of mothers (wild type infection, measles vaccine, or not immunised) because the non-immunised group was too small.…”

The Economic Value of Rotavirus Vaccination When Optimally Implemented in a High-Income Country

“…The model needs to replicate the observed data and must include those variables that affect the shape of the observed curve using direct and indirect vaccine effects [ 22 ]. The model splits the observation period into two linked consecutive time periods, using a different model structure for each period ( Figure 2 ).…”

“…The model splits the observation period into two linked consecutive time periods, using a different model structure for each period ( Figure 2 ). Full details of the model, including sensitivity analyses, have recently been presented [ 22 ] (see Appendix A and Appendix B for further details on the model input data and model construction).…”

“…Real-world data were collected in a special study set up in Belgium in 2007, called the Rotavirus Belgium Impact Study (RotaBIS) [ 18 ]. This study showed that there was seasonality in rotavirus infection spread (mostly between January and March); a vaccine herd effect early on; and potentially waning vaccine efficacy to consider when adequately fitting the observed with the modelled data [ 19 , 20 , 21 , 22 ]. Moreover, a vaccine catch-up programme to immunise the entire age group up to the age of 5 years was not possible, because the vaccine has a very low frequency of a serious side effect (intussusception) if the doses are not given within strict time schedules [ 23 , 24 ].…”

“…Therefore, continuous vaccination of new-born infants with high coverage from the start was needed to obtain control of the infection spread. The follow-up of the observed RotaBIS data identified two key points [ 22 ]. First, if the initiation of the vaccination programme was not optimal, this could lead to low vaccine coverage in the group forming the primary source of infection during the normal rotavirus peak season, with the consequence that the herd effect could be low (15%) in the first year and could disappear in the second year due to greater prominence of secondary sources for infection spread [ 21 ].…”