Making Data Talk: A Workbook for Effective Communication of Public Health Data, Lecture notes of Public Health

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U.S. DEPARTMENT

OF HEALTH AND

HUMAN SERVICES

National Institutes of Health

Making Data Talk:

A Workbook

National Cancer Institute

How to use this Workbook

This workbook provides an overview of the main points contained in the book Making Data Talk: Communicating Public Health Data to the Public, Policy Makers, and the Press , as well as practical exercises for applying the book’s concepts and communication principles to your unique situation.

The first three chapters review basic communication concepts, from analyzing your audience to building a storyline. Chapters 4 and 5 shift the focus from conceptual to practical by introducing guidelines for presenting data, as well as the O rganize, P lan, T est, and In tegrate (OPT-In) framework developed by the textbook’s authors to aid in planning and executing data-related communications. Chapters 6 and 7 focus on the application of concepts and the OPT-In framework to the real world in scenarios, such as crisis situations or advocacy.

The ultimate goal of this workbook—and the book Making Data Talk: Communicating Public Health Data to the Public, Policy Makers, and the Press —is to help you select and communicate quantitative data in ways lay audiences can understand. You will gain the most from this workbook by reviewing its contents in concert with the book Making Data Talk: Communicating Public Health Data to the Public, Policy Makers, and the Press , making note of the tips and guidelines it presents, and completing the practical exercises beginning in Chapter 3 to ensure your understanding of the concepts and ability to successfully apply them.

Table of Contents

• Introduction
• Chapter 1: You CAN Make Data Talk and Be Understood
  • Table 1.1 Contrasts Between Scientists and Lay Audiences
  • Table 1.2 Tips for Presenting Audience-Friendly Data
• Chapter 2: Use Communication Fundamentals to Your Advantage
  • Figure 2.1 Basic Communication Model
  • Table 2.1 Types of Sources
  • Table 2.2 Types of Channels
  • Table 2.3 Comparison of Selected Lay Audiences
• Chapter 3: Help Lay Audiences Understand Your Data
  • Table 3.1 Audience Biases that Influence Quantitative Data Processing
  • Practice Exercise
• Chapter 4: Present Data Effectively
  • Table 4.1 Basics of Visual Symbols
  • Practice Exercise
• Chapter 5: Use the OPT-In Framework to Make Your Data Talk
  • Table 5.1 Roles of Data in Communication
  • Practice Exercise
• Chapter 6: Show What You Know: Communicating Data in Acute Public Health Situations
  • Table 6.1 Acute Public Health Situations: Communication Phases and Objectives
  • Table 6.2 Questions Lay Audiences May Have in Acute Public Health Situations
  • Table 6.3 Higher-Controversy Situations: Characteristics and Communication Implications
  • Practice Exercise
    • Communicating Data in Health Policy or Program Advocacy Situations Chapter 7: Show What You Know:
  • Figure 7.1 Public Policy Cycle
  • Practice Exercise
• Conclusion
• References

Introduction

Communicating scientific data to lay audiences is difficult. Public health practitioners, researchers, clinicians, and others in the public health field often have the responsibility of communicating “the numbers” to individuals from all walks of life. 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?

The National Cancer Institute (NCI) is pleased to introduce Making Data Talk: A Workbook , which is based on the groundbreaking book Making Data Talk: Communicating Public Health Data to the Public, Policy Makers, and the Press.^1 This workbook is designed to be a companion piece that enhances the information presented in the text by Drs. David E. Nelson, Bradford W. Hesse, and Robert T. Croyle, NCI researchers with significant expertise in their own fields. The information presented in Making Data Talk: Communicating Public Health Data to the Public, Policy Makers, and the Press reflects a careful synthesis of research from many disciplines, so the principles described in the book can be applied to a variety of public health issues, not just cancer. This workbook complements the various communication and education tools and materials available through the NCI.

The content presented in the following chapters will take you through communication concepts, an easy-to-understand framework for communicating data, and the application of that framework to actual public health situations. Many chapters also include practice exercises that use real-world examples to reinforce key concepts and help you apply what you have learned. We hope this workbook will serve as a guide for those looking to successfully communicate scientific evidence to improve public health.

Office of Communications and Education National Cancer Institute

Sharing information with the public is now one of the standard responsibilities of scientists and public health practitioners, such as epidemiologists, researchers, statisticians, health care providers, public relations officers, and others. Communication is a complex process that involves a series of choices about how to convey what you know or discover in a way that others can understand and, if applicable, use to make decisions about their beliefs, attitudes, or behaviors.

The O rganize, P lan, T est, and In tegrate (OPT-In) framework (presented and explained in Chapter 5) helps health communicators organize the communication process. OPT-In relies on a variety of basic communication concepts, including audience analysis. In Chapter 1, audiences are discussed in terms of what they expect when receiving data and how those expectations can be used to craft more effective communication. After reading this chapter, you will be able to:

➥ Identify some of the differences between health communicators and their audiences.

➥ Explain some basic strategies for making data more audience-friendly.

You are likely to be successful if you use what

is known about your audiences

Effective communication starts with having a strong understanding of your audiences. It is important to note that the people with whom you wish to communicate have their own areas of expertise, but those areas of expertise may fall outside of science or public health. The scientific community shares a common culture, so people outside of that culture may not share the same terminology, beliefs, or interests. See Table 1.1 for more detail on some common differences between scientists and lay audiences.

CHAPTER ONE:

You CAN Make Data Talk and Be Understood

Table 1.2 Tips for Presenting Audience-Friendly Data

After reading this chapter, you should be able to recognize that effective communication with audiences outside of the scientific community requires consideration of how those audiences differ from the scientific community and how communication can be modified to account for those differences. For further detail on concepts presented in this chapter, refer to Chapter 1, Introduction, of Making Data Talk: Communicating Public Health Data to the Public, Policy Makers, and the Press.

Tip Example/Explanation

• Avoid terms not frequently used outside of the scientific community.

Cohort, longitudinal

• Avoid terms with multiple meanings. Surveillance
• Avoid science and math concepts that can be misunderstood. If these term(s) or concepts must be used, be sure to explain them in an easy-to-understand way.

Proportions, relative risk

• Focus on the main message instead of detailed scientific arguments or outcomes.

When making decisions, many people use heuristics (shortcuts) rather than the rational decision-making model used by most scientists.^2

• Explain how the data may impact audiences. Demonstrating impact can help audiences understand why the data are relevant to them.
• Present data in a distinctive way that helps you gain the attention of your audiences.

For a majority of people in the United States, health issues are of moderate-to-low interest.^3 Presenting relevant and interesting information can reduce the likelihood that people will filter it out due to lack of interest.

CHAPTER TWO:

Use Communication Fundamentals

to Your Advantage

All efforts to share information—whether discussing a simple issue or a complex topic—consist of a few basic communication elements. By understanding these elements and how they work together, you can make informed choices about your communication approach. After reading this chapter, you will be able to:

➥ Identify and differentiate the four main elements of the basic communication model.

➥ Name three lay audiences key to public health communication.

➥ Recognize how messages can be developed to support a storyline.

Consider the basics

A variety of elements are involved in the basic framework of communication. Although many more complex models of communication exist, this workbook uses the basic communication model presented in Figure 2. as the foundation for discussion.

Figure 2.1 Basic Communication Model

This basic communication model presents four main elements:

1) Messages, or WHAT is used to convey information (e.g., words, symbols, or pictures). 2) Sources (or senders), or WHO SENDS the message (e.g., individuals or organizations). 3) Channels, or HOW messages are sent (e.g., newspapers, conversations, or e-mail). 4) Audiences (or receivers), or WHO RECEIVES the message and interprets it.

Source and channel Message

Context

Context

Audience (receiver)

Source : Making Data Talk: Communicating Public Health Data to the Public, Policy Makers, and the Press by David E. Nelson, Bradford W. Hesse, and Robert T. Croyle (2009), Figure 2.1, p. 31. By permission of Oxford University Press, Inc. (www.oup.com). See References for additional sources.

These concepts are an important part of the OPT-In framework presented in Chapter 5, with storylines being crucial to the “Organize” step and message development being one of the five elements of the “Plan” step.

Sources

As noted in Table 2.1, sources are differentiated based on the intimacy of contact, with interpersonal sources involving one-on-one interaction and mediated sources involving one-to-many interactions. Communication often involves a mix of both interpersonal and mediated sources, such as when health information received from mass media (e.g., a radio talk show host) becomes part of interpersonal communication (e.g., conversations with friends).

Table 2.1 Types of Sources

Type Description Example

Interpersonal

sources

People who share information through one-on-one interaction

Family members, friends, colleagues, health care providers

Mediated

sources

People who share information through one-on-many interaction

Journalists, politicians

Channels

Like sources, channels can also be divided into two main types: interpersonal and mediated (see Table 2.2).

Table 2.2 Types of Channels

Channel selection is a key component of message development and distribution. Research shows that many health campaigns have failed because only a small percentage of the intended audience was actually exposed to the message(s).^4 To have a better chance of reaching the intended audience, scientists and health practitioners should consider the following factors:

➥ Availability,^ or whether audiences can access certain sources or channels (e.g., television, Internet,

personal health care provider).

➥ Preference,^ or where and how audiences obtain information, which is closely related to availability.

➥ Credibility,^ or how believable a source is, based on perceived trustworthiness and expertise.

Audience trends related to these factors change frequently, so you may want to consult the latest research to understand the current habits and behaviors of your intended audiences.

Audiences

The following lay audience segments are important to public health communication:

➥ General public:^ individuals within the population at large.

➥ Policy makers:^ administrators and elected officials with the authority to make decisions

that affect public health.

➥ Press:^ print, broadcast, or online journalists who obtain or report news.

Table 2.3 provides descriptions and characteristics of each of the three lay audiences.

Type Description Example

Interpersonal

channels

Ways of sharing information that involve personal contact

Phone conversations, oral presentations, personal e-mails, doctor visits, text messages, social media/networking

Mediated

channels

Ways of sharing information that are more impersonal and typically reach larger numbers of people at a time

Newspapers, newsletters, Web sites, TV

When people receive messages, they process and interpret them based on their own literacy level, tendencies, and biases. As a result, these factors must be considered and addressed when communicating quantitative data to audiences. After reading this chapter, you will be able to:

➥ Identify audience tendencies that can influence how people receive data.

➥ Describe biases that audiences can have when interpreting data.

➥ Recognize techniques to overcome these tendencies and biases.

Be aware of audience tendencies

People are not always well-prepared to receive and process messages containing quantitative data. Quantitative literacy (i.e., the skills required to apply mathematical operations) varies from person to person, and even the most educated audiences may have only a basic or intermediate level of familiarity with mathematical concepts. Common mistakes people make when interpreting numbers include:

➥ Misunderstanding probability estimates^5 (people may believe that a risk of 1 in 200 is greater than

a risk of 1 in 25).

➥ Misunderstanding percentages.

➥ Improperly converting proportions to percentages.^6

To account for differences in quantitative literacy, health communicators should simplify messages, provide additional explanation, or modify their approach to increase audience understanding.

In addition to literacy considerations, health communicators should also be aware of general information processing factors that, although not specific to data or public health topics, can be strongly influential as people process quantitative data. Here is a list of these tendencies along with explanations and examples.

Cognitive processing limits. Individuals have a limited capacity to process large amounts of information at one time and simplify or “chunk” the information to which they are exposed. ◆ The 7-digit telephone numbering system was based on research suggesting that people can optimally retain only 7 (±2) discrete pieces of information at a time.^7

Satisficing. People tend to limit the amount of mental energy they spend obtaining information until they believe they have “enough” for their purposes.^8 ◆ Studies show that visitors will usually leave a Web site within 15 minutes or less if they do not find the information they need.^9

CHAPTER THREE:

Help Lay Audiences Understand Your Data

Expectations of experts and the challenge of uncertainty. Most lay audiences want experts with experience and credentials to provide definitive, prescriptive information.^10 ◆ To use a non-health example, people look to mechanics to definitively diagnose automobile problems — instead of estimating that there is a 30 percent chance that the alternator is the problem — as well as to recommend specific solutions.

Processing risk information. Many people misunderstand concepts related to risk, such as absolute risk, lifetime risk, and cumulative risk.^11 ◆ Most people do not recognize that repetition of low-risk behavior — such as failing to wear a seat belt with every car ride — increases a person’s cumulative risk of adverse outcomes during their lifetime.

Framing. “Framing” is presenting data in a way that is consistent with common public frames or models. ◆ Emphasizing the possibility of colon cancer over the minor discomforts of a colonoscopy is an example of a loss frame. ◆ Associating rewards, such as losing weight and looking fit with exercise, is an example of a gain frame.

Scanning. People often do a quick scan of written or visual material to decide if it interests them, draw conclusions about what the major points might be, and try to identify the bottom line.^12 ◆ When an Internet search for specific information returns hundreds or thousands of potential Web sites, people scan the first few results before deciding which link to follow.

Use of contextual cues. People tend to look for cues to help them better process and understand information, especially in cases where the data presented is complex, detailed, or in an unfamiliar format.^13 ◆ Regular reports on breast cancer data can be of more use to audiences by highlighting what has changed since the last report.

Resistance to persuasion. People have a natural resistance to persuasion and often engage in a practice of defensive processing, an approach that blunts messages that are inconsistent with current behavior. ◆ Smokers may blunt messages emphasizing that smoking is bad since those messages are inconsistent with the smoker’s own attitude toward tobacco use.

Role of emotion. Emotions have the potential to be a motivating influence on behavior by heightening arousal, orienting attention, and prompting self-reflection.^14 ◆ Communicating that 440,000 Americans will die from smoking in a given year may cause a variety of emotional reactions based on the reader’s own relationship or attitude towards smoking.

Use strategies to overcome tendencies and biases

Health communicators can use a variety of factors about their audiences, from the characteristics discussed in Chapter 2 of this workbook to the quantitative literacy level, general tendencies, and mental shortcuts discussed in this chapter. Below are several tips that take these factors into consideration and can improve communication about public health data across a wide spectrum of groups:

Determine whether data should be presented. Are there sufficient data to support a science-based storyline? If so, are they appropriate for presentation to intended audiences?

Be brief and concise. Present the “bottom line” and use only a few data points to support it.

Be complete and transparent in portraying statistics. Word choice, as well as the selection or omission of data, can be highly influential in how audiences receive and interpret data. Avoid implication of a causal link between variables that are only associated through correlation.

Identify and counter mistaken health-related lay audience beliefs. Use messages that acknowledge the misconception, diplomatically state why it is inaccurate, and present an alternate explanation.

Use familiar types of data and explain key scientific or mathematical concepts. Choose formats that will likely be familiar (e.g., frequencies and round numbers) and supplement data that has the potential to be misunderstood (e.g., concepts of risk) with explanations or additional materials as needed.

Address uncertainty directly. Be honest about the tentative nature of the science, emphasize why scientists cannot make a definitive explanation, and work to answer questions about what uncertainty means for people.

Ensure usability. Select user-friendly formats (e.g., boxes that highlight key points, upfront summaries of information) so that audiences can process information more accurately and efficiently.

Provide contextual information. Present individual findings within their larger context, using tools such as comparison data and short text phrases that state the key findings as appropriate.

After reading this chapter, you should be more familiar with factors that can influence how people receive and interpret data. For further detail on concepts presented in this chapter, refer to Chapter 3, Overcoming General Audience Tendencies and Biases to Enhance Lay Understanding of Data, of Making Data Talk: Communicating Public Health Data to the Public, Policy Makers, and the Press.

Practice Exercise

The following five scenarios describe a situation where a communicator uses a specific communication skill or strategy to overcome an audience tendency or bias. Review the scenarios and select the answer which correctly identifies the tendency or bias that the communicator sought to overcome.

1) 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 persuasion b. Anchoring and adjustment bias c. Failure to consider randomness d. Satisficing

2) 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 five years. This graphic helps address the following: a. Processing of risk information b. Role of emotion c. Use of contextual cues d. Satisficing

3) A government health agency publishes a press release about a complex genetics research project. Although many endpoints were involved in the study, the communicator decides to focus only on one or two data points. This strategy is designed to address which of the following: a. Information framing effects b. Cognitive processing limits c. Use of contextual cues d. Role of emotion