Highlights
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The impact of graphics to inform the general public on health knowledge is unknown.
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Heat maps were evaluated as superior to dot maps or picto-trendlines.
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Heat maps are a viable option to widely disseminate information about infectious diseases.
Keywords: Influenza, Communication, Graphics, Vaccination, Infectious disease
Abstract
Background
Graphics are increasingly used to represent the spread of infectious diseases (e.g., influenza, Zika, Ebola); however, the impact of using graphics to adequately inform the general population is unknown.
Objective
To examine whether three ways of visually presenting data (heat map, dot map, or picto-trendline)—all depicting the same information regarding the spread of a hypothetical outbreak of influenza—influence intent to vaccinate, risk perception, and knowledge.
Design
Survey with participants randomized to receive a simulated news article accompanied by one of the three graphics that communicated prevalence of influenza and number of influenza-related deaths.
Setting
International online survey.
Participants
16,510 adults living in 11 countries selected using stratified random sampling based on age and gender.
Measurements
After reading the article and viewing the presented graphic, participants completed a survey that measured interest in vaccination, perceived risk of contracting disease, knowledge gained, interest in additional information about the disease, and perception of the graphic.
Results
Heat maps and picto-trendlines were evaluated more positively than dot maps. Heat maps were more effective than picto-trendlines and no different from dot maps at increasing interest in vaccination, perceived risk of contracting disease, and interest in additional information about the disease. Heat maps and picto-trendlines were more successful at conveying knowledge than dot maps. Overall, heat maps were the only graphic to be superior in every outcome.
Limitations
Results are based on a hypothetical scenario.
Conclusion
Heat maps are a viable option to promote interest in and concern about infectious diseases.
1. Introduction
Outbreaks such as the severe acute respiratory syndrome (SARS), H1N1 influenza, the Ebola virus, and the Zika virus have, at various times, seized public attention. While these diseases have affected different populations with varying severity, public knowledge of each outbreak has been spurred through multiple modalities—by traditional patient-healthcare provider relationships and also by news outlets [1], [2], local health departments [3], [4], [5], international health organizations [6], [7], [8], and social media [9], [10], [11]. For instance, during the H1N1, Ebola, and Zika outbreaks, topics discussed through social media included risk factors, prevention, experience, and disease trends [12], [13], [14], [15], [16].
To communicate disease prevalence and spread, visuals often matter more than words [17], [18], [19]. Graphics developed for public health purposes are widely disseminated through social media [20], [21]. Much like the spread of a disease, outbreak-related graphics and videos inspire a cascade of additional tweets and Internet searches [20], [22]. Therefore, the effect different visualization strategies have on the general public is particularly relevant to public health experts.
Yet, it remains unclear which types of graphics are most informative, preferred, and appropriate for the dissemination of public health information. On one hand, graphics are often able to circumvent problems with literacy and numeracy that may be encountered when attempting to communicate risk [23]. The use of graphics has repeatedly been demonstrated to improve patient understanding, enhance decision-making, and shape risk perception [24], [25]. On the other hand, the design of visuals can also have a negative impact on understanding and reactions to information. For example, even accurate graphics may distract individuals from relevant statistical information [26]. In addition, while most people may prefer the graphical presentation of data, graphics may affect perceptions of risk idiosyncratically [27], [28], [29]. Yet, graphics remain a cornerstone of the broad dissemination of public health knowledge [30], [31].
In this study, we focused on a critical health communication situation—how to communicate effectively to the public and to motivate preventive behaviors among those at the epicenter of an infectious disease (in this case, an influenza outbreak). We compared three ways of visually representing the same data about the spread of disease to determine which format resulted in the greatest differences in participants’ knowledge, risk perceptions, or desire to receive a future vaccination. In addition, we evaluated which graphic was preferred since proclivity to view the graphic is an important component to its effectiveness. To ensure that our approach would be generalizable across populations, we tested this question in an 11-country experiment, showing participants a simulated news article in the country’s primary language.
2. Methods
2.1. Study population
We recruited a stratified random sample of adults (age > 17) from a panel of Internet users administered by Survey Sampling International (SSI), which recruits panel members through various opt-in methods, including website banners, television advertisements, e-mails, apps, social media, and websites. SSI employs a probability-weighted random process to select panel members. For our study, quotas were established based on respondent age and gender to ensure that the sample was representative for each country. The sampling algorithm continued to recruit SSI participants until all quotas were achieved. Participants were recruited between February and March 2016. Incomplete surveys were excluded. Upon survey completion, participants were entered into drawings administered by SSI for modest prizes.
Subjects were recruited from the following countries: Finland, Germany, Hungary, Italy, the Netherlands, Norway, Poland, Spain, Sweden, the United Kingdom, and the United States and received surveys in the primary language of the country in which they resided.
2.2. Survey
Participants were asked to imagine an outbreak of influenza and then provided with a simulated news article that described the spread of influenza in their respective country. The article contained information regarding the influenza virus, its potential symptoms, and a vaccine being developed (Appendix). Articles were translated from English to the predominant language of the country. Articles were cross-randomized to provide participants with five varying communication strategies, such as symptom severity, confidence portrayed by a scientific expert, name used for the influenza strain, the use of metaphors to describe the spread of influenza, and type of graphic used to communicate prevalence of and deaths due to influenza. As none of these other communication strategies interacted with the type of graphic used to communicate prevalence, those results are not included.
This study focused on the use of three graphics: dot map, heat map, picto-trendline (Fig. 1 ). Dot maps, also known as dot density maps, use different sized circles on a map to represent differences in disease prevalence and to demonstrate a spatial pattern [32], [33]. Dot maps have been widely used in infectious disease communication by leading health organizations (e.g., the WHO and the CDC used dot and heat maps during the Ebola outbreak [30], [31]). Heat maps are displays that illustrate changes in disease prevalence by using color overlays for different regions. Using colors has been demonstrated to effectively illustrate uncertainty and variation in probability [34]. Picto-trendlines are a variant of pictographs, or icon-based displays of probabilities, that use lines to represent numbers of people affected [35].
Fig. 1.
Example of graphics provided to UK participants.
All participants received information in the graphics representing an increase in prevalence of influenza and number of influenza-related deaths over a three-month span of time. Prevalence of influenza and deaths due to influenza were derived from actual rates documented from the 2009 H1N1 influenza outbreak and were then extrapolated to each country based on its population. While the picto-trendline graphic only showed country-specific data, both the dot map and heat map graphics displayed country-specific data relative to all European countries. The participant’s country of origin always had the highest disease prevalence in comparison to other European countries, and case counts decreased as geographical distance increased. For example, German participants received a graphic with Germany having the highest prevalence of influenza and deaths due to influenza, followed by slightly lower rates in surrounding countries (i.e., Poland, the Netherlands, Czech Republic), and the lowest prevalence of influenza and deaths related to influenza in the countries farthest away (i.e., Spain, Ireland, Greece). Each graphic contained a legend to help orient participants to the information. All graphics were created using Adobe Illustrator and Microsoft Paint.
2.3. Data quality
All Survey Sampling International (SSI) participants undergo systematic quality controls prior to inclusion in any sample. For example, SSI uses digital fingerprinting to flag duplicate respondents. SSI performs continuous monitoring to assess for inappropriately quick responses or inattention. To confirm location, SSI uses two-factor authentication prior to reward redemption [36].
2.4. Outcomes
After reading the newspaper article, participants completed a survey that measured perceived likelihood of contracting influenza [defined by a discrete visual analog scale ranging from 1 (“Very unlikely”) to 7 (“Very likely”)], vaccination interest [defined by a discrete visual analog scale ranging from 1 (“Definitely would not get a vaccination”) to 7 (“Definitely would get a vaccination”)], interest in additional information about influenza [defined by a discrete visual analog scale ranging from 1 (“not at all”) to 7 (“a great deal”)], knowledge about information contained in the news article (percentage of two questions answered correctly about information presented in the graphic), graphic preference (aggregate measure of five items—perceived efficacy, helpfulness, science, interpretability, and trustworthiness of the graphic), and standard demographics (e.g., age, gender, education).
2.5. Statistical analysis
Chi-square tests were used to evaluate associations between the graphic received and respondent characteristics. Analysis of variance testing with Bonferroni correction was performed with each outcome as the dependent variable and the presented graphic as the independent variable. While there is controversy as to whether Likert-type data should be analyzed as continuous, ordinal, or dichotomous, the work of Norman and others has demonstrated ANOVA to be robust to non-parametric data [37].
To evaluate moderation effects, two-way analysis of variance testing was performed on each outcome as the dependent variable and the presented graphic as one independent variable and either recent vaccination history, country, and gender as the second independent variable. For age and education, analysis of covariance was used. All analyses were conducted using Stata 14.1 (College Station, TX).
The University of Michigan Medical Institutional Review Board deemed this study exempt from review.
2.6. Role of the funding source
This work was supported by the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement #278763 (AF) and NIH T32HL007749 (TSV). Funding sources had no role in the study conception, design, conduct, analysis, or manuscript construction.
3. Results
In total, 20,138 individuals started the survey. Of those who started, 16,510 individuals from 11 countries completed the survey (82% completion rate). Respondents took a median of 18 min (inter-quartile range 13–25 min) to complete the survey. Respondents were recruited from the following countries: Finland (n = 1554), Germany (n = 1546), Hungary (n = 998), Italy (n = 1509), the Netherlands (n = 1938), Norway (n = 764), Poland (n = 1509), Spain (n = 1604), Sweden (n = 1539), the United Kingdom (n = 1762), and the United States (n = 1787).
Among the respondents, the average age was 46.8 years (SD 16.2). Females comprised 49.8% of the respondents. The majority of respondents were married (60.7%). About 9.1% of respondents were healthcare workers, and 4999 (30.5%) respondents had received an influenza vaccination within the past two years (Table 1 ).
Table 1.
Respondent characteristics by graphic received.
| Type of graphic received |
All respondents | |||
|---|---|---|---|---|
| Heat map | Dot map | Picto-trendline | ||
| Respondents | 5553 (33.6) | 5476 (33.2) | 5481 (33.2) | 16,510 |
| Age | ||||
| <35 | 1500 (27.6) | 1451 (27.0) | 1483 (27.6) | 4434 (27.4) |
| 35–50 | 1436 (26.4) | 1419 (26.4) | 1370 (25.5) | 4225 (26.1) |
| 50–59 | 860 (15.9) | 869 (16.2) | 898 (16.7) | 2630 (16.3) |
| 60+ | 1639 (30.1) | 1632 (30.4) | 1625 (30.2) | 4896 (30.3) |
| Gender | ||||
| Male | 2774 (50.2) | 2687 (49.4) | 2648 (48.6) | 8109 (49.4) |
| Female | 2715 (49.2) | 2700 (49.6) | 2754 (50.6) | 8169 (49.8) |
| Other | 33 (0.6) | 52 (1.0) | 43 (0.8) | 128 (0.8) |
| Married | 3364 (60.9) | 3297 (60.5) | 3316 (60.8) | 9977 (60.7) |
| Healthcare worker | 518 (9.4) | 479 (8.9) | 481 (8.9) | 1478 (9.1) |
| Vaccinated within past 2 years | 1705 (30.9) | 1666 (30.6) | 1628 (29.9) | 4999 (30.5) |
| Region | ||||
| Finland | 535 (9.6) | 513 (9.4) | 506 (9.2) | 1554 (9.4) |
| Germany | 512 (9.2) | 509 (9.3) | 525 (9.6) | 1546 (9.4) |
| Hungary | 348 (6.3) | 327 (6.0) | 323 (5.9) | 998 (6.0) |
| Italy | 505 (9.1) | 509 (9.3) | 495 (9.0) | 1509 (9.1) |
| The Netherlands | 647 (11.7) | 624 (11.4) | 667 (12.2) | 1938 (11.7) |
| Norway | 259 (4.7) | 258 (4.7) | 247 (4.5) | 764 (4.6) |
| Poland | 500 (9.0) | 509 (9.3) | 500 (9.1) | 1509 (9.1) |
| Spain | 532 (9.6) | 532 (9.7) | 540 (9.9) | 1604 (9.7) |
| Sweden | 507 (9.1) | 518 (9.5) | 514 (9.4) | 1539 (9.3) |
| United Kingdom | 597 (10.8) | 592 (10.8) | 573 (10.5) | 1762 (10.7) |
| United States | 611 (11.0) | 585 (10.7) | 591 (10.8) | 1787 (10.8) |
After reading the simulated newspaper article, respondents indicated that they preferred heat maps and picto-trendlines versus dot maps (4.90 vs. 4.93 vs. 4.40, P < 0.001) (Fig. 2 , Table 2 , and Appendix Table 1). Additionally, respondents who received the heat map were significantly more likely to be interested in vaccination (4.67 vs. 4.56, P = 0.01), perceive a greater likelihood of contracting influenza (3.62 vs. 3.50, P < 0.001), and be interested in more information about influenza (5.27 vs. 5.20, P = 0.04) compared to those who received a picto-trendline. Overall, knowledge obtained from graphics was low; however, respondents who received heat maps (25.4%) and picto-trendlines (25.4%) were significantly more likely to answer knowledge questions correctly than those who received dot maps (22.7%) (P < 0.001). Respondents who received the dot map reported a significantly higher perceived likelihood of contracting influenza (3.58 vs. 3.50, P = 0.03) than those who received a picto-trendline, but dot maps did not differ significantly from heat maps or picto-trendlines for all other outcomes (all P > 0.06) (Table 2 and Appendix Table 1).
Fig. 2.
Graphic preferences by country.
Table 2.
Association between graphics and outcomes.
| Measure | Heat map* | Picto-trendline | Dot map | F test | P value |
|---|---|---|---|---|---|
| Evaluation of graphic | 4.90 (1.32)+ | 4.93 (1.37)+ | 4.40 (1.42) | 258.66 | <0.001 |
| Vaccination intention | 4.67 (2.00)# | 4.56 (2.03) | 4.64 (2.02) | 4.37 | 0.01 |
| Perceived likelihood of contracting influenza | 3.62 (1.64)# | 3.50 (1.62)+ | 3.58 (1.64) | 7.55 | <0.001 |
| Interested in more information | 5.28 (1.70)# | 5.20 (1.74) | 5.27 (1.71) | 3.88 | 0.02 |
| Knowledge (% correct) | 25.4% (32.9)+ | 25.4% (33.7)+ | 22.7% (32.0) | 11.63 | <0.001 |
Mean (SD).
P < 0.05 vs. Dot Map.
P < 0.05 vs. Picto-trendline Map.
For the outcome of graphic preference, there was an interaction between the graphic received and country of the respondent (P < 0.001). This moderating effect was due to differences in whether heat maps or picto-trendlines were rated higher than the other; dot maps were preferred least in all countries (Fig. 2). However, graphic preference was not moderated by recent vaccination history, education, age, country, or gender (all P > 0.10).
4. Discussion
In an international survey, we demonstrated that communicating disease prevalence through heat maps and picto-trendlines were preferred over dot maps. However, heat maps increased interest in vaccination, perceived risk of contracting influenza, and desire for additional information about influenza compared to picto-trendlines.
Data visualization has increased the popularity of alternative methods to presenting statistical information. For instance, projects such as Gapminder [38] have developed animations of world income distribution and global health that are easy to understand. However, to our knowledge, this work is the first to experimentally evaluate visualization strategies to communicate infectious disease prevalence to the general public. Numerous studies have evaluated the impact of graphics on risk communication [24], [25]. However, this prior work has primarily focused on communicating to individual patients—particularly the risks and benefits individuals would receive as a result of a medical procedure. While epidemiologists have long used graphical displays to illustrate the spread of disease [39], [40], that work has focused on delivering information to highly trained public health specialists. In contrast, this study assesses graphics that communicate disease prevalence to the general population, simulating that their country is at the epicenter of an infectious disease outbreak. This type of communication is especially relevant to public health and government officials seeking to provide broadly accessible health information. As the lay public receives more of its health information multi-dimensionally, from healthcare experts, news, and social media, it is critically important to evaluate the most appropriate graphics used to communicate disease prevalence, spread, and risk.
Understanding optimal communication strategies is of particular importance in relation to influenza, where vaccination greatly reduces the risk of contracting disease [41], [42]; yet, vaccination rates remain suboptimal [43]. Prior research related to public health communication and influenza has demonstrated that the general public prefers brief, balanced, and evidence-based messages [44], [45], [46]. However, there is currently no standard method for the graphical communication of epidemiological data to the lay public. The WHO and the CDC used a combination of dot and heat maps during the recent Ebola outbreak to depict prevalence [30], [31]. Dot maps appear most often in the literature [32], [47], [48], [49]. However, heat maps have been discussed infrequently in the literature. In one study of Europeans interested in weather forecasts, a color contour map featuring hypothetical forecasts was preferred over six other more typical formats of graphical presentation of data, such as bar graphs, pie charts, and line graphs [50]. Yet, we found that while both heat maps and picto-trendlines were preferred over dot maps, the use of heat maps to demonstrate the spread of disease increased interest in vaccination compared to the use of picto-trendlines and no differently than dot maps. Furthermore, dot maps appeared to be the worst method to convey information, as respondents with dot maps had less knowledge gained from the graphic compared to those with heat maps and picto-trendlines. Thus, unlike dot maps and picto-trendlines, heat maps achieved the trifecta—greater respondent preference, increased interest in vaccination, and better translation of knowledge.
Graphics must also be carefully examined to assess both their potential positive and negative impact. Visuals can produce a wide variety of emotional responses that may be difficult to predict [23], [27]. Furthermore, it is often difficult to adequately illustrate uncertainty in risk communication [51]; however, this becomes even more imperative when presenting graphics at the population level, since risk may vary greatly between individuals and across locations. Experimental evidence remains limited as to how different graphics are processed and understood [51]; therefore, additional research is necessary to evaluate the impact of public health graphics aimed at the general population. In particular, it is unclear how other modes of communication, such as animations and videos, impact public health communication. Our prior work using animation in the context of cancer treatment options suggests that animated graphics may result in suboptimal risk communication [52].
This study must be interpreted in the context of several limitations. First, we chose three graphics to present to participants, but other graphics not presented in this study could be better suited to communicate disease prevalence. However, these graphics were chosen based on a pilot study in which these three graphics were the most promising. In addition, this study used graphics to accompany text, which may have a different effect than graphics viewed in isolation. Second, each respondent viewed a single graphic; however, no single graphic may suit the communication needs of all individuals. Furthermore, in this study, the picto-trendline graphic contained data only on the participant’s country, compared to heat maps and dot maps, which included data on surrounding countries. However, including additional data in the picto-trendline graphic may increase its complexity and decrease its efficacy. Third, while differences seen in this study were statistically significant, it is difficult to project whether they are clinically relevant. Fourth, preferences in this study were developed based on a hypothetical scenario, which may not correspond with preferences during an actual pandemic of influenza. For example, we examined interest in future vaccination, which may not be perfectly correlated with actual vaccination. However, vaccination intentions are strongly associated with subsequent vaccination [53]. Additional research is necessary to determine how these graphics affect actual vaccination rates. Finally, our study sampled participants from Europe and the United States and may not be generalizable to developing nations.
Despite these limitations, these findings have implications for public health officials, clinicians, and the general public. As health information continues to be disseminated in a multitude of channels, additional research is necessary to understand the best way to present meaningful data. Graphics have the potential to improve public health, but our research demonstrates that not all graphics are equally effective. Heat maps may provide a means to generate more widespread interest, awareness for evidence-based treatments, desire for more information, and convey relevant knowledge.
5. Conclusion
Among a large international sample, the use of heat maps to demonstrate the spread of infectious disease increased interest in vaccination, knowledge, and desire to learn more about the disease. Of the tested graphics, heat maps were the best option to increase vaccination rates and promote infectious disease awareness.
Acknowledgements
Author Contributions: Dr. Fagerlin had full access to all of the data in the study and takes full responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Fagerlin, Scherer, Knaus, Das, Zikmund-Fisher.
Acquisition of data: Fagerlin, Knaus.
Analysis and interpretation of data: Fagerlin, Valley, Scherer, Knaus, Das, Zikmund-Fisher.
Drafting of the manuscript: Fagerlin, Valley.
Critical revision of the manuscript for important intellectual content: Fagerlin, Valley, Scherer, Knaus, Das, Zikmund-Fisher.
Statistical analysis: Scherer.
Obtained funding: Fagerlin.
Conflict of interest disclosures: No disclosures were reported.
Funding/Support: This work was supported by the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement #278763 and NIH T32HL007749 (TSV).
Role of the sponsors: The funding organizations had no role in the design and conduct of the study; in the collection, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.
Disclaimer: This manuscript does not necessarily represent the view of the U.S. Government or the Department of Veterans Affairs.
Special Acknowledgement: The authors would like to give special thanks to Elastique, an award-winning visual design company in Germany, for their assistance in creating the graphics used for this study.
Footnotes
Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.vaccine.2017.05.048.
Appendix A. Supplementary material
References
- 1.Lehmann B.A., Ruiter R.A., Kok G. A qualitative study of the coverage of influenza vaccination on Dutch news sites and social media websites. BMC Public Health. 2013;13(1):547. doi: 10.1186/1471-2458-13-547. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Mollema L., Harmsen I.A., Broekhuizen E. Disease detection or public opinion reflection? Content analysis of tweets, other social media, and online newspapers during the measles outbreak in the Netherlands in 2013. J Med Internet Res. 2015;17(5):e128. doi: 10.2196/jmir.3863. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Huesch M.D., Galstyan A., Ong M.K., Doctor J.N. Using social media, online social networks, and internet search as platforms for public health interventions: a pilot study. Health Serv Res. 2016;51(Suppl 2):1273–1290. doi: 10.1111/1475-6773.12496. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Thackeray R., Neiger B.L., Smith A.K., Van Wagenen S.B. Adoption and use of social media among public health departments. BMC Public Health. 2012;12(1):242. doi: 10.1186/1471-2458-12-242. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Hawker M.D. Social networking in the National Health Service in England: a quantitative analysis of the online identities of 152 primary care trusts. Stud Health Technol Inform. 2010;160(Pt 1):356–360. [PubMed] [Google Scholar]
- 6.Gesualdo F., Romano M., Pandolfi E. Surfing the web during pandemic flu: availability of World Health Organization recommendations on prevention. BMC Public Health. 2010;10(1):561. doi: 10.1186/1471-2458-10-561. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Strekalova Y.A. Health risk information engagement and amplification on social media: news about an emerging pandemic on facebook. Health Educat Behav. 2016 doi: 10.1177/1090198116660310. [DOI] [PubMed] [Google Scholar]
- 8.Duncan B. How the media reported the first days of the pandemic (H1N1) 2009: results of EU-wide media analysis. Euro Surveill. 2009;14(30):19286. doi: 10.2807/ese.14.30.19286-en. [DOI] [PubMed] [Google Scholar]
- 9.Hale T.M., Pathipati A.S., Zan S., Jethwani K. Representation of health conditions on Facebook: content analysis and evaluation of user engagement. J Med Internet Res. 2014;16(8):e182. doi: 10.2196/jmir.3275. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Hawn C. Take two aspirin and tweet me in the morning: how Twitter, Facebook, and other social media are reshaping health care. Health Aff (Millwood) 2009;28(2):361–368. doi: 10.1377/hlthaff.28.2.361. [DOI] [PubMed] [Google Scholar]
- 11.Keller B., Labrique A., Jain K.M., Pekosz A., Levine O. Mind the gap: social media engagement by public health researchers. J Med Internet Res. 2014;16(1):e8. doi: 10.2196/jmir.2982. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Odlum M., Yoon S. What can we learn about the Ebola outbreak from tweets? Am J Infect Control. 2015;43(6):563–571. doi: 10.1016/j.ajic.2015.02.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Househ M. Communicating Ebola through social media and electronic news media outlets: a cross-sectional study. Health Informatics J. 2016;22(3):470–478. doi: 10.1177/1460458214568037. [DOI] [PubMed] [Google Scholar]
- 14.Chew C., Eysenbach G. Pandemics in the Age of Twitter: content analysis of tweets during the 2009 H1N1 Outbreak. Sampson M, ed. PLoS One. 2010;5(11):e14118. doi: 10.1371/journal.pone.0014118. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Fung I.C.-H., Duke C.H., Finch K.C. Ebola virus disease and social media: a systematic review. Am J Infect Control. 2016 doi: 10.1016/j.ajic.2016.05.011. [DOI] [PubMed] [Google Scholar]
- 16.Fu K.-W., Liang H., Saroha N., Tse Z.T.H., Ip P., Fung I.C.-H. How people react to Zika virus outbreaks on Twitter? A computational content analysis. Am J Infect Control. 2016 doi: 10.1016/j.ajic.2016.04.253. [DOI] [PubMed] [Google Scholar]
- 17.Magnan R.E., Cameron L.D. Do young adults perceive that cigarette graphic warnings provide new knowledge about the harms of smoking? Ann Behav Med. 2015;49(4):594–604. doi: 10.1007/s12160-015-9691-6. [DOI] [PubMed] [Google Scholar]
- 18.Hammond D., Fong G.T., Borland R., Cummings K.M., McNeill A., Driezen P. Text and graphic warnings on cigarette packages. Am J Prev Med. 2007;32(3):202–209. doi: 10.1016/j.amepre.2006.11.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Eden K.B., Dolan J.G., Perrin N.A. Patients were more consistent in randomized trial at prioritizing childbirth preferences using graphic-numeric than verbal formats. J Clin Epidemiol. 2009;62(4):415–424. doi: 10.1016/j.jclinepi.2008.05.012. e3. [DOI] [PubMed] [Google Scholar]
- 20.Seltzer E.K., Jean N.S., Kramer-Golinkoff E., Asch D.A., Merchant R.M. The content of social media’s shared images about Ebola: a retrospective study. Public Health. 2015;129(9):1273–1277. doi: 10.1016/j.puhe.2015.07.025. [DOI] [PubMed] [Google Scholar]
- 21.Ahmed O.H., Lee H., Struik L.L. A picture tells a thousand words: A content analysis of concussion-related images online. Phys Ther Sport. 2016;21:82–86. doi: 10.1016/j.ptsp.2016.03.001. [DOI] [PubMed] [Google Scholar]
- 22.Towers S., Afzal S., Bernal G. Mass media and the contagion of fear: the case of Ebola in America. Ouzounis CA, ed. PLoS One. 2015;10(6):e0129179. doi: 10.1371/journal.pone.0129179. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Tait A.R., Voepel-Lewis T., Zikmund-Fisher B.J., Fagerlin A. Presenting research risks and benefits to parents: does format matter? Anesth Analg. 2010;111(3):718–723. doi: 10.1213/ANE.0b013e3181e8570a. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Hawley S.T., Zikmund-Fisher B., Ubel P., Jancovic A., Lucas T., Fagerlin A. The impact of the format of graphical presentation on health-related knowledge and treatment choices. Patient Educ Couns. 2008;73(3):448–455. doi: 10.1016/j.pec.2008.07.023. [DOI] [PubMed] [Google Scholar]
- 25.Field R.I. What you see is what you fear. Hum Vaccin Immunother. 2013;9(12):2670–2671. doi: 10.4161/hv.26653. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Zikmund-Fisher B.J., Dickson M., Witteman H.O. Cool but Counterproductive: interactive, web-based risk communications can backfire. J Med Internet Res. 2011;13(3):e60. doi: 10.2196/jmir.1665. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Tait A.R., Voepel-Lewis T., Zikmund-Fisher B.J., Fagerlin A. The effect of format on parents’ understanding of the risks and benefits of clinical research: a comparison between text, tables, and graphics. J Health Commun. 2010;15(5):487–501. doi: 10.1080/10810730.2010.492560. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Zikmund-Fisher B.J., Fagerlin A., Ubel P.A. What’s Time Got to Do with It? Inattention to duration in interpretation of survival graphs. Risk Anal. 2005;25(3):589–595. doi: 10.1111/j.1539-6924.2005.00626.x. [DOI] [PubMed] [Google Scholar]
- 29.Witteman H.O., Fuhrel-Forbis A., Wijeysundera H.C. Animated randomness, avatars, movement, and personalization in risk graphics. J Med Internet Res. 2014;16(3):e80. doi: 10.2196/jmir.2895. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.World Health Organization. Ebola maps. WHO; 2016. <http://who.int/csr/disease/ebola/maps/en/ [accessed November 21, 2016].
- 31.Centers for disease control and prevention. Ebola Virus Disease Distribution Map. CDC; 2016. <>. [accessed November 21, 2016].
- 32.Barreto M.L. The dot map as an epidemiological tool: a case study of Schistosoma mansoni infection in an urban setting. Int J Epidemiol. 1993;22(4):731–741. doi: 10.1093/ije/22.4.731. [DOI] [PubMed] [Google Scholar]
- 33.Severtson D.J., Burt J.E. The influence of mapped hazards on risk beliefs: a proximity-based modeling approach. Risk Anal. 2012;32(2):259–280. doi: 10.1111/j.1539-6924.2011.01700.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Leitner M., Buttenfield B.P. Guidelines for the display of attribute certainty. Cartogr Geogr Inf Sci. 2000;27(1):3–14. [Google Scholar]
- 35.Fagerlin A., Wang C., Ubel P.A. Reducing the influence of anecdotal reasoning on people’s health care decisions: is a picture worth a thousand statistics? Med Decis Mak. 2005;25(4):398–405. doi: 10.1177/0272989X05278931. [DOI] [PubMed] [Google Scholar]
- 36.Survey Sampling International. Data Quality | Market Research and Survey Data Quality - SSI; 2017. <https://www.surveysampling.com/technology/data-quality/ [accessed May 15, 2017].
- 37.Norman G. Likert scales, levels of measurement and the “laws” of statistics. Adv Heal Sci Educ. 2010;15(5):625–632. doi: 10.1007/s10459-010-9222-y. [DOI] [PubMed] [Google Scholar]
- 38.Rosling H., Zhang Z. Health advocacy with Gapminder animated statistics. J Epidemiol Glob Health. 2011;1(1):11–14. doi: 10.1016/j.jegh.2011.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Karlsson D., Ekberg J., Spreco A., Eriksson H., Timpka T. Visualization of infectious disease outbreaks in routine practice. Stud Health Technol Inform. 2013;192:697–701. [PubMed] [Google Scholar]
- 40.Turner A.M., Reeder B., Ramey J. Scenarios, personas and user stories: User-centered evidence-based design representations of communicable disease investigations. J Biomed Inform. 2013;46(4):575–584. doi: 10.1016/j.jbi.2013.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.World Health Organization. Influenza (Seasonal) Fact Sheet. WHO; 2014. <> [accessed October 25, 2016].
- 42.Grijalva C.G., Zhu Y., Williams D.J. Association between hospitalization with community-acquired laboratory-confirmed influenza pneumonia and prior receipt of influenza vaccination. JAMA. 2015;314(14):1488–1497. doi: 10.1001/jama.2015.12160. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.European Centre for Disease Prevention and Control. Seasonal Influenza Vaccination in Europe - Overview of Vaccination Recommendations and Coverage Rates in the EU Member States for the 2012-13 Influenza Season; 2015. 10.2900/693898.
- 44.Poland G.A. The 2009–2010 influenza pandemic: effects on pandemic and seasonal vaccine uptake and lessons learned for seasonal vaccination campaigns. Vaccine. 2010;28:D3–D13. doi: 10.1016/j.vaccine.2010.08.024. [DOI] [PubMed] [Google Scholar]
- 45.Mowbray F., Marcu A., Godinho C.A., Michie S., Yardley L. Communicating to increase public uptake of pandemic flu vaccination in the UK: Which messages work? Vaccine. 2016;34(28):3268–3274. doi: 10.1016/j.vaccine.2016.05.006. [DOI] [PubMed] [Google Scholar]
- 46.Bish A., Yardley L., Nicoll A., Michie S. Factors associated with uptake of vaccination against pandemic influenza: A systematic review. Vaccine. 2011;29(38):6472–6484. doi: 10.1016/j.vaccine.2011.06.107. [DOI] [PubMed] [Google Scholar]
- 47.Smith C.M., Le Comber S.C., Fry H., Bull M., Leach S., Hayward A.C. Spatial methods for infectious disease outbreak investigations: systematic literature review. Eurosurveillance. 2015;20(39):30026. doi: 10.2807/1560-7917.ES.2015.20.39.30026. [DOI] [PubMed] [Google Scholar]
- 48.Martinez B.F., Annest J.L., Kilbourne E.M., Kirk M.L., Lui K.J., Smith S.M. Geographic distribution of heat-related deaths among elderly persons. Use of county-level dot maps for injury surveillance and epidemiologic research. JAMA. 1989;262(16):2246–2250. [PubMed] [Google Scholar]
- 49.Eng S.B., Werker D.H., King A.S. Computer-generated dot maps as an epidemiologic tool: investigating an outbreak of toxoplasmosis. Emerg Infect Dis. 1999;5(6):815–819. doi: 10.3201/eid0506.990613. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Taylor A.L., Dessai S., de Bruin W.B. Communicating uncertainty in seasonal and interannual climate forecasts in Europe. Philos Trans Roy Soc London A Math Phys Eng Sci. 2015;373 doi: 10.1098/rsta.2014.0454. 2055. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Spiegelhalter D., Pearson M., Short I. Visualizing uncertainty about the future. Science (80-) 2011;333(6048):1393–1400. doi: 10.1126/science.1191181. [DOI] [PubMed] [Google Scholar]
- 52.Zikmund-Fisher B.J., Witteman H.O., Fuhrel-Forbis A., Exe N.L., Kahn V.C., Dickson M. Animated graphics for comparing two risks: a cautionary tale. J Med Internet Res. 2012;14(4):e106. doi: 10.2196/jmir.2030. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Witteman H.O., Chipenda Dansokho S., Exe N., Dupuis A., Provencher T., Zikmund-Fisher B.J. Risk Communication, Values Clarification, and Vaccination Decisions. Risk Anal. 2015;35(10):1801–1819. doi: 10.1111/risa.12418. [DOI] [PubMed] [Google Scholar]
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