State Example: Translating Infant Mortality Toolkit Content

1. This slide set, provided by Elizabeth J Conrey, PhD, RD, is an example of how the content from the Infant Mortality Toolkit can be translated for training public health practitioners • The slides are a subset from the course titled: The Epidemiology of Maternal and Infant Health for State and Local Practitioners, given at the Ohio State University Summer Program in Population Health • Examples provided are from the Ohio Department of Health State Example: Translating Infant Mortality Toolkit Content

2. Content from Day 5 - Toolkit Chapter Addressed: • Communicating Your Findings • The slides include detail from one of the key resources cited in the toolkit chapter, Making Data Talk: Communicating Public Health Data to the Public, Policy Makers, and the Press • This slides pull content from the accompanying workbook published by the National Cancer Institute to describe the techniques you can use to select and communicate quantitative data in ways lay audiences can understand State Example: Translating Infant Mortality Toolkit Content

3. The Epidemiology of Maternal and Infant Health for State and Local Practitioners Elizabeth J Conrey, PhD, RD July 22-26, 2015 The Ohio State University, 2015 Summer Program in Population Health

4. Communicating Public Health Data

5. Making Data Talk • Demand for health information continues  • Skill of public health professionals to provide =? • Better data communication will  chances that findings influence public health practice

6. http://www.choiceproject.wustl.edu/

7. Making Data Talk OBJECTIVES • Summarize selection and presentation of public health data • Provide practical suggestions on how to better communicate with the public, policy makers, & the press http://www.cancer.gov/cancertopics/cancerlibrary/MDT-Workbook.pdf

8. Making Data Talk • How do you summarize and convey data so they make sense to someone who may not be familiar with the topic, let alone the basics of epidemiology or statistics? • How do you package and present data to answer the question often asked by busy people with competing demands and time constraints: why should I care?

9. You CAN Make Data Talk and Be Understood • Sharing information with the public is a public health RESPONSIBILITY • Communication COMPLEX Series of choices: How to convey what you know / or findings or analyses so that audience can understand AND make decisions for programs / practices / policies?

10. Plain Writing & Clear Communication • Plain Writing Act of 2010 • Requires federal agencies to write plainly when communicate with public • www.plainlanguage.gov • Health Literacy • Ability to get, process, & understand basic health info/svcs to make sound health decisions • www.cdc.gov/healthliteracy • Clear Communication Index • To assess and develop communication products for general public • Use if you write/edit/design/review • www.cdc.gov/ccindex/index

11. OPT-In Framework • Helps you organize communication process Organize Plan Test Integrate

12. Use what you know about your audience • 3 Important Audiences to public health • General Public • Policy Makers • Press • Each has expertise in something • Each outsider to culture of the scientific community

13. Contrasts between Scientists and Lay Audiences

14. Expectations for receiving scientific data • Why should I believe or do what you recommend? • How did you reach your conclusion (rationale)? [I already have a belief about this and I’m not going to change my mind just because you tell me to] • What do I do with this information? • What action should I take?

15. ETHICS • Many lay audiences trust scientists/experts • Responsibility to maintain that trust • Careful not to mislead or omit • Lead people to conclusions based on sound data that are well-reasoned and well-presented

16. Tips for Presenting Audience-Friendly Data

17. Use Communication Fundamentalsto Your Advantage • Basic communication elements/model • Key Audiences • Storyline (how messages can support one)

18. Basic Communication Model

19. Basic Communication Model CONSIDER: Purpose, Strategy, & Context WHOSENDS the message (individual, organization) WHORECEIVES the message and interprets it (general public, lawmaker) WHAT is used to convey information (words, symbols, pictures) HOW messages are sent (email, presentation, newspaper)

20. Purpose: why is the message being communicated? Four most common in public health • Increase knowledge • Instruct • Facilitate informed decision-making • Persuade Which applies to the message you are sending??

21. Strategy: what is the approach to gain attention? • ACTIVE! • Media campaign • Encourage word of mouth • Town halls • PASSIVE • Post information to be found by those who are seeking • PUSH-PULL • Uses both

22. CONTEXT: What factors influence receipt / interpretation? • Often outside sender’s control • Influence at multiple points • Can include • Other sources of information (i.e., Jenny McCarthy) • Personal experience (I put my first child to sleep on their stomach) • Cultural beliefs, values, misperceptions (We take care of ourselves) • Competing priorities (Food)

23. MESSAGES • Support a STORYLINE STORYLINE– major conclusions that scientists / health practitioners want audience to understand. Bottom line. • Each message • “Chunk” of information • Based on scientific knowledge and understanding • Stands alone, BUT • Collectively provides rationale for storyline (main theme)

24. MESSAGES Consider if your story is supported by • “Settled Science” PURSUASIVE / INSTRUCTIVE • Limited supporting knowledge or no consensus INFORM DECISION-MAKING PROCESS

25. SOURCES: Types

26. CHANNELS: Types

27. CHANNELS Consider • Availability (access??) • Preference (how obtain information?) • Credibility (believable, trustworthy?) • Change frequently ! Consult latest research to understand habits/behaviors of audience

28. AUDIENCES • General public • Policy Makers (including administrators in your own agency!) • Authority to make decisions that affect public health • Press

29. COMPARING LAY AUDIENCES • Individual characteristics • Occupational and institutional factors • Regular sources of information

30. Help Lay Audiences Understand Your Data • Audience tendencies (can influence perception) • Biases • Techniques to overcome tendencies and biases

31. Be Aware of Audience Tendencies • Rarely prepared to process messages containing qualitative data • Quantitative literacy varies • Probability estimates (1 in 200 vs. 1 in 25) • Percentages • Converting proportions into percentages • SIMPLIFY Messages, or Provide additional explanation, or MODIFY approach

32. Common Mistakes when Interpreting Numbers • Misunderstanding probability estimates • Which risk is greater? 1 in 200 or 1 in 20 • Misunderstanding Percentages • Improperly converting proportions to percentages To overcome quantitative literacy differences a. simplify message b. provide additional information, or c. modify approach to increase audience understanding

33. Information Processing: General Factors Cognitive Processing Limits • Limited capacity to process large amounts of information at once • So people “chunk” (e.g., phone numbers)

34. Information Processing: General Factors Satisficing • We limit mental energy spent on obtaining information • Stop when its “good enough” for purposes

35. Information Processing: General Factors Expectations of Experts and Challenge of Uncertainty • Lay audiences want experts with experience & credentials to provide definitive, prescriptive information • Is it your alternator? Or 30% chance that it’s the alternator?

36. Information Processing: General Factors Processing Risk Information • Misunderstanding of concepts related to risk • Absolute risk • Lifetime risk • Cumulative risk • e.g., repetition of low risk behavior increases cumulative risk over lifetime

37. Information Processing: General Factors Framing • Consistency with common public frames or models? • Loss Frame: Possibility of colon cancer over minor discomforts of colonoscopy • Gain Frame: Associate rewards (losing weight, looking fit) with exercise

38. Information Processing: General Factors Scanning • Quick scan of written or visual material to decide if it interests them • Draw conclusions about major points • Try to identify the bottom line

39. Information Processing: General Factors Use of Contextual Clues • People look for clues to help process & understand new info • Especially when complex, detailed, or new format

40. Information Processing: General Factors Resistance to Persuasion • Natural resistance to persuasion • Often engage in “defensive processing” • Approach that blunts messages inconsistent with current behavior

41. Information Processing: General Factors Role of Emotion • Motivating influence on behavior • Heighten arousal • Orient attention • Prompt self-reflection

42. Be Aware of Audience Biases • Representativeness heuristic • Anchoring and adjustment bias • Correlation = causation • Failure to consider randomness

43. Strategies to overcome tendencies and biases • Determine whether data should be presented • Be brief and concise • Be complete and transparent in portraying statistics • Identify and counter mistaken health-related audience beliefs • Use familiar types of data and explain key scientific or mathematical concepts • Address uncertainty directly • Ensure usability • Provide contextual information

44. A university research department decides not to release findings from a phase I clinical trial because of concern that the promise of a pharmaceutical treatment showing that 80% of participants had complete resolution of their disease symptoms may create great excitement that will be followed by disappointing results in phase II. This decision shows a consideration for which of the following?a. Resistance to persuasionb. Anchoring and adjustment biasc. Failure to consider randomnessd. Satisficing

45. To help explain a new report that conveys the latest statistics related to breast cancer incidence, communicators develop a graphic that compares this year’s figures to figures from the previous 5 years. This graphic helps address the following:a. Processing of risk informationb. Role of emotionc. Use of contextual cluesd. Satisficing

46. During a media interview, a study’s lead scientist answers a question related to the brain’s role in the development of addiction. After the reporter takes notes, the scientist reiterates that a particular brain area doesn’t cause addiction, but that it plays a role in the development of addiction. This shows the scientists attempt to overcome which of the following?a. Information framing effectsb. Processing of risk informationc. Failure to consider randomnessd. Correlation equals causation

47. A doctor conducts an interview to discuss health conditions affecting women. During the interview, the doctor acknowledges that many women perceive breast cancer to be the primary killer of women. He provides statistics showing that heart disease kills more women than breast cancer and then reiterates that women should be just as aware of heart disease as breast cancer. This technique helps overcome the following:a. Resistance to persuasionb. Scanningc. Failure to consider randomnessd. Anchoring and adjustment bias

48. Presenting Data Effectively • Think more about what you want your audience to understand and less about what you want to say • Use communication tools to help people build knowledge • Do it accurately and ethically

49. Perception is Everything • People consider items close together in a visual field to be related • Can you use this to promote understanding • Eyes follow lines and directions implied by separate elements within visual field • Line graph vs. bar graph? • subheadings • People “fill in” information to help them make sense • Avoid allowing to fill in with wrong information • Test messages

50. Communicate with various methods • Text labels • Verbal qualifiers • Metaphors • Narratives