Assessing the impact of vaccination programmes on burden of disease: Underlying complexities and statistical methods

It is important to assess the impact a vaccination programme has on the burden of disease after it is implemented. For example, this may reveal herd immunity effects or vaccine-induced shifts in the incidence of disease or in circulating strains or serotypes of the pathogen. In this article we summarise the key features of infectious diseases that need to be considered when trying to detect any changes in the burden of diseases at a population level as a result of vaccination efforts. We outline the challenges of using routine surveillance databases to monitor infectious diseases, such as the identification of diseased cases and the availability of vaccination status for cases. We highlight the complexities in modelling the underlying patterns in infectious disease rates (e.g. presence of autocorrelation) and discuss the main statistical methods that can be used to control for periodicity (e.g. seasonality) and autocorrelation when assessing the impact of vaccination programmes on burden of disease (e.g. cosinor terms, generalised additive models, autoregressive processes and moving averages). For some analyses, there may be multiple methods that can be used, but it is important for authors to justify the method chosen and discuss any limitations. We present a case study review of the statistical methods used in the literature to assess the rotavirus vaccination programme impact in Australia. The methods used varied and included generalised linear models and descriptive statistics. Not all studies accounted for autocorrelation and seasonality, which can have a major influence on results. We recommend that future analyses consider the strength and weakness of alternative statistical methods and justify their choice.