Evidence Synthesis to inform health related decision making

In order to make decisions about management of diseases it is necessary to understand how diseases develop and spread and how interventions will impact on them. This often requires us to identify and combine many sources of information so that we can take robust decisions relevant to the clinical or public health context of interest. For example, when the objective is to control an infectious disease in the community we need to know how many people are affected by the disease (prevalence) and in which age groups; how it is currently spreading (incidence); how it is transmitted (transmission and infectivity); and the geographical location where the disease is more prevalent. This information will feed into relevant interventions, such as vaccination or treatment. This would require understanding to which subgroups of the population vaccination should be given first, or for which patients is a particular treatment cost-effective. Data on each of these aspects will typically come from several studies, possibly with different formats and not directly interpretable. Robust statistical methods are then needed to integrate such multiplicity of evidence in a coherent manner to make it useful input for decision making.