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# Table 1 Terms and definitions in risk communication

Term | Definition |
---|---|

Simple frequency format
| Expresses the event rate as an integer with an appropriate denominator (e.g. x in 100) |

Simple percentage format
| Expresses the event rate as a percentage (e.g. x%) |

Natural frequency format
| The term ‘natural frequencies’ was proposed for estimating the probability arising from a joint occurrence of events (e.g. the probability of having breast cancer given an abnormal mammography result). Natural frequencies preserve the base rate of the outcome (e.g. breast cancer) and report the ‘actual’ or ‘natural’ number of people having a particular outcome (e.g. having a positive test result). An example would be “Out of every 10,000 people, 30 have colorectal cancer. Of these, 15 will have a positive haemoccult test. Out of the remaining 9970 people without colorectal cancer, 300 will still test positive. How many of those who test positive actually have colorectal cancer? Answer: 15 out of 315” |

Conditional probabilities
| An alternative representation of this information is the conditional probability format. For example: “The probability of having colorectal cancer is .003%. Of people who have the cancer, 50% get a positive test result. Of people who do not have cancer, 3% will nevertheless test positive. What is the probability that a person who tests positive has colorectal cancer? Answer: 4.8%”. |

Bayesian reasoning
| Infers the post-probability of outcome from the prior probability and a likelihood function. |

Tailored health communication
| Refers to providing information to a person based on characteristics that are unique to that person. It is assumed that tailored messages are perceived as more relevant to an individual and are therefore better processed and understood. Tailoring information using an individual’s specific risk factors might likewise increase people’s involvement with the information and lead to a better understanding. |

Aleatory uncertainty
| It is concerned with the randomness or indeterminacy of future events. |

Epistemic uncertainty
| On the other hand, this is the lack of knowledge needed to predict future outcomes, also known as “ambiguity” and is concerned with the lack of reliability, credibility, or adequacy of risk information. A primary example is imprecision in risk estimates which are typically expressed by confidence intervals. |

Pictographs (sometimes called icon arrays)
| They are visual graphic display formats which aim to represent the size of both the numerator and denominator in the one diagram. In other words, they show the part-whole relationship. Examples include systematic ovals, 100 face or human figure diagrams and displays where event icons are scattered rather than grouped. |

Numeracy
| It is the ability to understand and apply mathematical concepts. |

Patient narratives
| Stories, also called testimonials, about individuals’ experiences or health outcomes, usually told from a first-person perspective. |