Synthesising public health evidence

Abstract

Public Health research is multi-disciplinary, complex and tries to understand problems in a 'real-world' context and this can make it hard to apply to practice and services that aim to improve health outcomes. Increasingly it has been realised that the mass of health evidence generated needs to be synthesised effectively. This chapter will explore the growing focus on this issue, the tools developed to synthesis evidence well and examples of evidence synthesis in practice. After reading this chapter you will be able to: • Define the meaning of research and research process • Understand the need for public health evidence synthesis • Describe the tools and techniques used to synthesise evidence effectively Before we can start to synth esise evidence we need to have some understanding of what evidence is and where the new evidence being explored comes from. Fundamentally as Lomas et al. (2005, p. 1) suggest 'evidence concerns facts (actual or asserted) intended for use in support of a conclusion.' Decision makers tend to view evidence colloquially, that is evidence is anything that can give a reason for believing something relevant is considered evidence. Researchers will tend to view evidence scientifically, it must be produced by robust, systematic and replicable methods that are clearly defined. So evidence is something that can be used to support a conclusion, but it is not the same as a conclusion (Lomas et al. 2005). Evidence can, and should, support decision making but the collection of evidence alone is not going to make the decisions. Evidence for Public Health impacts and interventions is generated through the process of research Research is about generating new information, doing some-thing new, collecting information to answer specific research questions and testing ideas or hypotheses. There are several characteristics of good research It should be: • Systematic: there is an agreed system for performing observations and measurement • Rigorous: the agreed system is followed exactly. • Reproducible: all the techniques, apparatus and materials used in making observations and measurements are written down in enough detail to allow other to reproduce the same process. • Repeatable: researchers often repeat their observations and measurements several times in order to increase the reliability of the data. (Bruce et al. 2008).