@incollection{Bodemer2012Commu-27920,
  year={2012},
  doi={10.1007/978-94-007-1433-5_24},
  title={Risk Communication in Health},
  isbn={978-94-007-1432-8},
  publisher={Springer Netherlands},
  address={Dordrecht},
  booktitle={Handbook of Risk Theory},
  pages={621--660},
  editor={Roeser, Sabine and Hillerbrand, Rafaela and Sandin, Per and Peterson, Martin},
  author={Bodemer, Nicolai and Gaissmaier, Wolfgang}
}

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    <dc:contributor>Bodemer, Nicolai</dc:contributor>
    <dc:contributor>Gaissmaier, Wolfgang</dc:contributor>
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    <dcterms:bibliographicCitation>Handbook of risk theory : epistemology, decision theory, ethics, and social implications of risk ; Band 2 / Sabine Roeser ... [et al.] (eds.). - Dordrecht [u.a.] : Springer, 2012. - S. 621-660. - ISBN 978-94-007-1432-8</dcterms:bibliographicCitation>
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    <dcterms:abstract xml:lang="eng">Policy makers, health professionals, and patients have to understand health statistics to make informed medical decisions. However, health messages often follow a persuasive rather than an informative approach and undermine the idea of informed decision making. The current practice of health risk communication is often biased: Risks are communicated one sided and in nontransparent formats. Thereby, patients are misinformed and misled. Despite the fact that the public is often described as lacking basic statistical literacy skills, statistics can be presented in a way that facilitates understanding. In this chapter, we discuss how transparent risk communication can contribute to informed patients and how transparency can be achieved. Transparency requires formats that are easy to understand and present the facts objectively. For instance, using statistical evidence instead of narrative evidence helps patients to better assess and evaluate risks. Similarly, verbal probability estimates (e.g., “probable,” “rare”) usually result in incorrect interpretations of the underlying risk in contrast to numerical probability estimates (e.g., “20%,” “0.1”). Furthermore, we will explain and discuss four formats – relative risks, conditional probabilities, 5-year survival rates, and single-event probabilities – that often confuse people, and propose alternative formats – absolute risks, natural frequencies, annual mortality rates, and frequency statements – that increase transparency. Although research about graphs is still in its infancy, we discuss graphical visualizations as a promising tool to overcome low statistical literacy. A further challenge in risk communication is the communication of uncertainty. Evidence about medical treatments is often limited and conflicting, and the question arises how health professionals and laypeople deal with uncertainty. Finally, we propose further research to implement the concepts of transparency in risk communication.</dcterms:abstract>
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