Developing a vaccination evaluation model to support evidence-based decision making on national immunization programs

Among all public health provisions national immunization programs (NIPs) are beyond doubt one of the most effective in reducing mortality, morbidity, and costs associated with major infectious diseases. To maintain their success, NIPs have to modernize in response to many new and old demands regarding efficacy, safety, availability of new vaccines, emerging and evolving pathogens, waning immunity, altered epidemiological situations, and the public's trust in the program. In this paper we present an evaluation model in the form of a checklist that may help in collecting relevant scientific information that is necessary for evaluation and decision making when considering changes in a NIP. Such a checklist points to relevant information on the vaccine-preventable disease, the pathogen causing it, the vaccine, and the cost-effectiveness ratio of the vaccine. However, the final judgment on a potential change in the NIP cannot be based on a simple algorithm, as the relevant information reflects factors of a very different kind and magnitude, to which different value judgements may be added, and which may have certain degrees of uncertainty. Because any change in the NIP may be accompanied by more or less unforeseen changes in the vaccine's efficacy, evolutionary consequences, including the antigenic composition of the pathogen, and the vaccine's safety profile, an intensive surveillance program should accompany any NIP. Elements thereof include clinical–epidemiological surveillance, surveillance of vaccination coverage, immune surveillance, surveillance of microbial population dynamics, and surveillance of adverse events and safety issues. We emphasize that the decision to introduce a vaccine in the NIP should be taken as seriously, both scientifically and ethically, as the decision to withhold a vaccine from the NIP. In the latter case one might be responsible for vaccine-preventable disease and mortality.