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Health Literacy: how to explain risk? Dr. Keiko Yasukawa University of Technology, Sydney

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Risk communication An important aspect of helping patients make informed decisions A communication process: of explaining, learning & confirming understanding by both the clinician and the patient

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Risk A measure of uncertainty A mathematical (probabilistic) concept A measure that is only one of many factors informing a patient’s decision It is nevertheless important that the mathematical concept of the risk impacting on the patient is communicated accurately and effectively

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Challenges to communicating risk Risk statistics and probabilities more generally can be represented in a number of different ways: As a fraction – 2/5 As a decimal -.4 As a percentage – 40% As a frequency - 4 in 10

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Example of different representations of risk statistics Lung cancer statistics Incidence In 2009, lung cancer was the 5th most commonly diagnosed cancer in Australia (after prostate, bowel, breast and melanoma of the skin), accounting for 8.9 per cent of all new cancers in Australia. 1 For men and women separately, lung cancer is the 4th most commonly diagnosed cancer for men (after prostate, bowel and melanoma of the skin) and for women (after breast, bowel and melanoma of the skin). 1 In 2009, there were 10,193 new cases of lung cancer diagnosed (6,034 in men; 4,159 in women). 1 In 2009, the risk of developing lung cancer before the age of 85 was 1 in In 2020, an estimated 13,640 people are expected to be diagnosed with lung cancer in Australia. 2 Accessed 2 April, 2014 from cancer/cancer-types/lung-cancer/lung-cancer-statisticshttp://canceraustralia.gov.au/affected- cancer/cancer-types/lung-cancer/lung-cancer-statistics

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Different types of risk information Single event probabilities Conditional probabilities Relative risks Ref. G Gigerenzer & A Edwards, 2004, Simple tools for understanding risks: from innumeracy to insight, BMJ, vol 327, 27 Sep

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A single event probability Example: ‘You have a 30% chance of a side effect from this drug’. Alternative approach: ‘Three out of every 10 patients have a side effect from this drug’. (Gigerenzer & Edwards, 2003, p 741)

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Conditional probabilities Example: The probability that a woman has breast cancer is 0.8%. If she has breast cancer, the probability that a mammogram will show a positive result is 90%. If a woman does not have breast cancer the probability of a positive result is 7%. A woman has a positive result. What is the probability that she actually has breast cancer? (Gigerenzer & Edwards, 2003, p 742) Alternative approach: ‘Eight out of every 1000 women have breast cancer. Of these eight women with breast cancer, seven will have a positive result on mammography. Of the 992 women who do not have breast cancer some 70 will still have a positive mammogram.’ A sample of women have positive mammograms. How many of these women actually have breast cancer? (Gigerenzer & Edwards, 2003, p 742)

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Conditional probabilities Alternative approach: ‘Eight out of every 1000 women have breast cancer. Of these eight women with breast cancer, seven will have a positive result on mammography. Of the 992 women who do not have breast cancer some 70 will still have a positive mammogram.’ A sample of women have positive mammograms. How many of these women actually have breast cancer? (Gigerenzer & Edwards, 2003, p 742) Explanation & answer to the question: Use the 1000 women as the reference class. Of the 1000, seven of the eight who have breast cancer will have a positive result. Of the 1000, 70 of the 992 women who do not have breast cancer will have a positive result. Of the 1000, a total of 77 will have a positive result, and out of this group of 77, seven have breast cancer. So, seven out of 77, or one in 11 will have breast cancer.

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Relative risks Example ‘If four out of every 1000 women (age 40 or older) who do not undergo mammography screening die of breast cancer, compared with three out of every 1000 who are screened, the benefit is often presented as a relative risk: “Mammography reduces breast cancer mortality by 25%”.’ Alternative approach: ‘In every 1000 women who undergo screening one will be saved from dying of breast cancer.’ (Gigerenzer & Edwards, 2003, p 741)

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Other strategies Use a combination of different modes of communication – Verbal – Symbolic/ written – Visual Eg 4 in 10 dying might be shown as: X X X X X Eg A 25% reduction from a statistic of 4 in 10 dying might be shown as: X X X X X X X X X X X X X X X X X X X X

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Mode of communication matters 2 or Something as ‘simple’ as two fifths can be misunderstood. In some languages, a fraction is read from the bottom up – ie five parts, two of

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Check for understanding Ask questions to: ₋elicit the patient’s understanding of the information you provided them ₋identify any misconceptions ₋identify other factors that may be interacting with the patient’s interpretation of the risk

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Key points Risk is a complex concept Communication and learning take time Communicating effectively takes practice There are many other factors influencing the patient’s understanding that could interfere or aid their understanding of risk But the risk of unsuccessful risk communication may be catastrophic!

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Reference and some resources Edwards, A, Elwyn, G & Mulley, A (2002) Explaining risks: turning numerical data into meaningful pictures, BMJ, vol 324, pp Fagerlin, A, Zikmund-Fischer, BJ & Ubel, PA 2011, Help[ing patients decide: ten steps to better risk communication, JNCI, vol 103, no 19, pp1-8. Gigerenzer, G (2003) Reckoning with risk: learning to live with uncertainty, Penguin, London. Gigerenzer, G & Edwards, A (2003) Simple tools for understanding risks: from innumeracy to insight, BMJ, vol 327, Harding Centre for Risk Literacy,

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