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Making data provocative and personal

Explore the benefits and pitfalls of using data in a provocative and personal manner. Learn how to appeal to your audience and avoid misleading presentations. Discover the power of data visualization.

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Making data provocative and personal

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  1. Making data provocative and personal Wendy Rice, Public Health Intelligence Analyst Nujcharee Haswell, Data Scientist North Yorkshire County Council Data and Intelligence Team

  2. Provocative and personal? Provocative (adj) - causing thought about an interesting subject Personal (adj) – relating or belonging to a single or particular person rather than to a group or organisation

  3. What does that mean? So, for data to be provocative AND personal, it needs to cause thought about an interesting subject, but presented in a way that appeals to individuals. This doesn’t mean it needs to be ABOUT individuals, just that the data needs to be presented in a way that make people relate to it.

  4. Learning outcomes Demonstrate the benefits of using data to appeal to your target audience Provide examples of how to (and how not to!) use data in a provocative and personal manner Describe common missteps to avoid Provide demonstration on data visualisation

  5. Provocative and personal?

  6. Provocative, personal, wrong? One of the most damaging public health examples of using data in a way that was provocative, personal and wrong.

  7. Provocative, personal, and misleading? Headlines like this are common-place. But what does it mean?? Presenting relative risk – gives you good headlines, but doesn’t give you the full story. People need realistic estimates of benefits and harms to be able to make decisions about their health.

  8. Understanding of benefits and harms…* 2009 survey of over 10,000 citizens from nine European countries Asked in face-to-face interviews about the benefits of prostate and breast cancer screening. About 90% of those surveyed OVERESTIMATED the benefits of screening by up to two-hundredfold or didn’t know. How can people get it so wrong? Under 10% of people surveyed accurately understood the benefits of screening. Is the data being presented in a misleading way? *Taken from Gigerenzer and Gray (ed), 2011. “Better Doctors, Better Patients, Better Decisions”. MIT Press.

  9. Example* It is thought that the framing of information can lead to this misperception. In a telephone survey in NZ, respondents given information on three different screening tests for unspecified cancers. Benefits were IDENTICAL but how they were presented differed (relative risk, absolute risk, number needed to treat). If you have this test every two years, it will reduce your chance of dying from this cancer by around one-third over the next ten years. If you have this test every two years, it will reduce your chance of dying from this cancer from around 3 in 1,000 to around 2 in 1,000 over the next 10 years. If around 1,000 people have this test every two years, 1 person will be saved from dying from this cancer every 10 years. How do you think people responded? Which one would most likely lead to people being willing to be screened? *Taken from Gigerenzer and Gray (ed), 2011. “Better Doctors, Better Patients, Better Decisions”. MIT Press.

  10. Results….. If you have this test every two years, it will reduce your chance of dying from this cancer by around one-third over the next ten years. If you have this test every two years, it will reduce your chance of dying from this cancer from around 3 in 1,000 to around 2 in 1,000 over the next 10 years. If around 1,000 people have this test every two years, 1 person will be saved from dying from this cancer every 10 years. Presented with option 1 – 80% (N= 306) said they would likely accept this test. Options 2 and 3 – 53% and 43% respectively said they would accept the test

  11. “Newspapers like big numbers and eye-catching headlines. They need miracle cures and hidden scares, and small percentage shifts in risk will never be enough for them to sell readers to advertisers…to this end, they pick the single most melodramatic and misleading way of describing any statistical increase in risk, which is called the ‘relative risk increase’.” Ben Goldacre, Bad Science, 2008

  12. How does this happen? Don’t worry! Physicians and health professionals also fall prey to this – a 2007 meta-analysis by Covey showed that medical professionals, students, and patients were all consistently manipulated by this sort of data representation. Relative risks may make better headlines, but they don’t give us any information about baseline risk. This is particularly an issue for low probability risks – for example, if the baseline is 2 people out of 10,000, a 50% reduction would be 1 person. So while the headline below is eye-catching, what does it tell us?

  13. North Yorkshire Next part of the workshop, going to go a bit more local…but still important! Give some context about how you can use local data as well as insight into how we use local data. But first…a bit of a plug…. NORTH YORKSHIRE CONNECT!

  14. North Yorkshire Connect The site is in effect a community directory The thing about community directories are that they become more useful the more people use them. Site continues to be developed, with contributors submitting events for people to see. But, there is also a cool data part (of course). The underlying data on the site – from the contributors to the site – allows NYCC to support social prescription. Better publicised information about support and services = more people likely to connect to the support that they need. Using data to make data personal!

  15. Life expectancy Life expectancy is essentially saying that a baby born today would be expected in the specified area to live to a certain age. NY – current life expectancy at birth is 81 for males and 84 for females That is probably neither provocative nor personal! If I just say…. Male life expectancy at birth in Harrogate for 2015-2017 is 81 and for women is 84. Somewhat interesting? But is it provocative? Is it personal?

  16. Healthy life expectancy This is where we could possibly look at healthy life expectancy…. Healthy life expectancy is meant to provide us with a richer picture but not just looking at the years people LIVE but the years they live in good health. So while two areas can have similar life expectancies, they might have different experiences of years lived with good health (healthy life expectancy).

  17. Harrogate – HLE and LE, by gender

  18. But can we make it more provocative and personal??

  19. Thank you! https://www.northyorkshireconnect.org.uk/

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