Title: Assistant Professor
University: Auburn University
Email: candicew@udel.edu
Twitter: candicewe
Website: https://candicewelhausen.com
Description of Work:
My research focuses primarily on the ways that data visualizations created to communicate quantitative risk information about epidemic disease shape how knowledge about disease, illness, and health is constructed. I’m particularly interested in how different viewers (e.g., public health experts and non-expert, public audiences) may use these visuals to make particular kinds of health-related decisions.
I am currently working on a book project [working title “The Picture of (Public) Health: The Past, Present, and Future of Data Visualizations of Epidemic Disease”], which investigates quantitative representations of epidemic disease in Western cultures and the ways in which these visuals have been used to constructed knowledge about disease and illness historically as well as in modern practice.
Symposium Submission:
At Your Own Risk: Data Visualizations, Participatory Surveillance, and an Emergent DIY Risk Assessment Ethic
Abstract
Following its inception in the 1980s, risk communication research initially focused on developing language-based strategies for conveying risk information to non-expert audiences. Within the next couple of decades, however, increasing attention was directed toward visual communication like graphics that represent quantitative information about health-related risks (see Lipkus & Hollands, 1999; Ancker et al., 2006). Many of these studies have lent insight into the ways that non-experts perceive this visual information, offering design strategies that may be effective in achieving particular communication objectives. At the same time, this focus on ‘accurately’ conveying numeric risk information to non-experts tends to privilege the perspective of experts who evaluate risks primarily through “qualitative and quantitative measures” (Dransch, Rotzoll & Poser, 2010, p. 296). In contrast, non-experts often employ psychometric factors (see Covello, Peters, Wojtecki, & Hyde, 2001, p. 385; Fischhoff, Slovic, Lichtenstein, Read, & Combs, 1978; Sandman, 1987; Slovic, 1987) to determine how dangerous a risk might be well as how much anxiety it might invoke (see Sandman, 2014) rather than considering how likely the risk is to occur. Further, research in visual risk communication also tends to assume that such data visualizations are usually created by experts (and sometimes other professionals) and then disseminated to non-experts who play no role in the construction of this information.
In this paper, I argue that the emergence of participatory surveillance technologies in the past ten years like wearable fitness devices and digital apps that track potential public health threats (e.g., HealthMap, Flu Near You, Sick Weather) disrupts how the creation of visual risk communication has traditionally been understood. Prompted in part by technological advances, the move toward a patient-centered model of care (Institute of Medicine, 2001), and the growing emphasis on preventative medicine (e.g., Office of Disease Prevention and Health Promotion, 2017), I suggest that participatory surveillance technologies have facilitated a “do-it-yourself (DIY)” risk assessment ethic.
More specifically, many fitness and health trackers now allow users to collect, visualize, and analyze information about a wide range of physical and mental health indictors: diet, activity, sleep quantity and quality, mindfulness. Some of these behaviors—diet and activity, for instance—have been linked to developing chronic health conditions later in life like diabetes and cardiovascular disease. By tracking select health-related behaviors through apps linked to these devices, users can visualize patterns and trends in their overall lifestyle choices. This information in turn can then be used to make particular kinds of decisions like engaging in healthy eating habits, reducing one’s salt intake, and/or getting more exercise in order to reduce the risk of developing a chronic disease.
In a somewhat similar vein, participatory disease-tracking systems like healthmap.org and flunearyou.org allow public audiences to simultaneously contribute to and visualize risk information about potential public health threats. Flu Near You, for example, was developed by a team of public health researchers and information technological professionals and enables the general public to voluntarily report flu-related symptoms they may be experiencing. Registered participants can submit weekly flu reports and then use the program’s user-contributed mapping feature to “know when the flu is around” in order to visually assess their risk. Indeed results from a user survey conducted by the program and shared with me (C. Nyugen, personal communication, August 17, 2016) suggest that this feature is particularly important for some participants. As one respondent positively stated: “I love FluNearYou and depend on the reports to see what is happening with the Flu around my area!,”
At the same time, the use of such technologies also does not invalidate or otherwise render visual numeric information about health risks created by experts obsolete or unimportant. In the first half of 2016, for instance, as the “pandemic potential” increased for Zika, (Lucey & Gostin, 2016, p. 865) a mosquito-borne virus that has been linked to some severe neurological conditions (CDC, 2016), public health agencies created a number of thematic maps targeted to non-experts that communicated visual risk information about the spread of the virus. Rather, I propose that visual risk information created through participatory surveillance efforts affords non-experts the agency to identify, measure, and assess risks in ways that they believe are most applicable to them, essentially enabling these readers/viewers to curate their own risk information experience.
References
Ancker, J. S., Senathiraja, Y., Kukafka, R., & Starren, J.B. (2006). Design features of graphics in health risk communication: A systematic review. Journal of the Medical Informatics Association, 13(6), 608-618. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1656964/
Centers for Disease Control and Prevention. (2016). Zika virus. Health effects and risks. Retrieved from https://www.cdc.gov/zika/healtheffects/index.html
Covello, V. T., Peters, R. G., Wojtecki, J. G., & Hyde, R. C. (2001). Risk communication, the West Nile virus epidemic, and bioterrorism: Responding to the communication challenges posed by the intentional or unintentional release of a pathogen in an urban setting. Journal of Urban Health: Bulletin of the New York Academy of Medicine, 78(2), 382-91.
Dransch, D., Rotzoll, H., & Poser, K. (2010). The contribution of maps to the challenges of risk communication to the public. International Journal of Digital Earth, 3(3), 292-311.
Fischhoff, B., Slovic, P., Lichtenstein, S., Read, S., & Combs, B. (1978). How safe is safe enough? A psychometric study of attitudes towards technological risks and benefits. Policy Sciences, 9(2), 127-152.
Institute of Medicine (2001). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academy Press.
Lipkus, I. M., & Hollands, J. G. (1999). The visual communication of risk. Journal of the National Cancer Institutes Monographs, 25, 149-163.
Lucey, D. R., & Gostin, L. O. (2016). The emerging Zika pandemic: enhancing preparedness. JAMA, 315(9), 865-866.
Office of Disease Prevention and Health Promotion. (2017). HealthyPeople.gov Retrieved from https://www.healthypeople.gov/
Sandman, P. (1987). Risk communication: Facing public outrage. EPA Journal, 21-22. Retrieved from http://www.psandman.com/articles/facing.htm
Sandman, P. M. (2014). Dr. Peter M. Sandman Introduction to risk communication and orientation to this website. Retrieved from http://www.psandman.com/index-intro.htm
Slovic, P. (1987). Perception of Risk. Science New Series, 236 (4799), 280-285.