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<article xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" article-type="brief-report"><?properties open_access?><front><journal-meta><journal-id journal-id-type="nlm-ta">Emerg Infect Dis</journal-id><journal-id journal-id-type="iso-abbrev">Emerging Infect. Dis</journal-id><journal-id journal-id-type="publisher-id">EID</journal-id><journal-title-group><journal-title>Emerging Infectious Diseases</journal-title></journal-title-group><issn pub-type="ppub">1080-6040</issn><issn pub-type="epub">1080-6059</issn><publisher><publisher-name>Centers for Disease Control and Prevention</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">30882322</article-id><article-id pub-id-type="pmc">6433032</article-id><article-id pub-id-type="publisher-id">17-1408</article-id><article-id pub-id-type="doi">10.3201/eid2504.171408</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Letter</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Letter</subject></subj-group><subj-group subj-group-type="TOC-title"><subject>Prior Vaccination and Effectiveness of Communication Strategies Used to Describe Infectious Diseases</subject></subj-group></article-categories><title-group><article-title>Prior Vaccination and Effectiveness of Communication Strategies Used to Describe Infectious Diseases</article-title><alt-title alt-title-type="running-head">Prior Vaccination and Effectiveness of Communication Strategies Used to Describe Infectious Diseases</alt-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Valley</surname><given-names>Thomas S.</given-names></name></contrib><contrib contrib-type="author"><name><surname>Scherer</surname><given-names>Aaron M.</given-names></name></contrib><contrib contrib-type="author"><name><surname>Knaus</surname><given-names>Megan</given-names></name></contrib><contrib contrib-type="author"><name><surname>Zikmund-Fisher</surname><given-names>Brian J.</given-names></name></contrib><contrib contrib-type="author"><name><surname>Das</surname><given-names>Enny</given-names></name></contrib><contrib contrib-type="author"><name><surname>Fagerlin</surname><given-names>Angela</given-names></name></contrib><aff id="aff1">University of Michigan, Ann Arbor, Michigan, USA (T.S. Valley, A.M. Scherer, M. Knaus, B.J. Zikmund-Fisher); </aff><aff id="aff2">University of Iowa, Iowa City, Iowa, USA (A.M. Scherer); </aff><aff id="aff3">Radboud University Nijmegen, Nijmegen, the Netherlands (E. Das); </aff><aff id="aff4">University of Utah School of Medicine, Salt Lake City, Utah, USA (A. Fagerlin); </aff><aff id="aff5">Informatics Decision-Enhancement and Analytic Sciences (IDEAS 2.0) Center for Innovation, Salt Lake City (A. Fagerlin)</aff></contrib-group><author-notes><corresp id="cor1">Address for correspondence: Thomas S. Valley, University of Michigan, Division of Pulmonary and Critical Care Medicine, 2800 Plymouth Rd, Bldg 16-G028W, Ann Arbor, MI 48109, USA; email: <email xlink:href="valleyt@umich.edu">valleyt@umich.edu</email></corresp></author-notes><pub-date pub-type="ppub"><month>4</month><year>2019</year></pub-date><volume>25</volume><issue>4</issue><fpage>821</fpage><lpage>823</lpage><abstract><p>We tested the effect of prior vaccination on response to communication strategies in a hypothetical news article about an influenza pandemic. Vaccinated were more likely than nonvaccinated participants to plan future vaccination, and future vaccination intent was greater with certain communication strategies. Using these findings to target communication may increase vaccination rates.</p></abstract><kwd-group kwd-group-type="author"><title>Keywords: </title><kwd>communication</kwd><kwd>influenza</kwd><kwd>human</kwd><kwd>immunization</kwd><kwd>vaccination</kwd><kwd>viruses</kwd><kwd>vaccines</kwd></kwd-group></article-meta></front><body><p>Vaccination rates for influenza remain surprisingly low (<xref rid="R1" ref-type="bibr"><italic>1</italic></xref>). Despite goals to vaccinate 75% of high-risk Europeans by 2010, &#x0003c;50% had been vaccinated in 2013 (<xref rid="R2" ref-type="bibr"><italic>2</italic></xref>). The reluctance of at-risk persons to receive vaccinations highlights the challenge of broadly vaccinating the general public.</p><p>Improving communication strategies that clinicians and healthcare organizations use to increase vaccination rates is cost-effective (<xref rid="R3" ref-type="bibr"><italic>3</italic></xref>). Yet randomized trials to improve influenza vaccination rates by improving physicians&#x02019; communication skills (<xref rid="R4" ref-type="bibr"><italic>4</italic></xref>) or by using various public health messages (<xref rid="R5" ref-type="bibr"><italic>5</italic></xref>) have not succeeded. Several studies have examined the effect of various communication strategies to improve vaccination rates for influenza (<xref rid="R6" ref-type="bibr"><italic>6</italic></xref>&#x02013;<xref rid="R9" ref-type="bibr"><italic>9</italic></xref>). However, the greatest predictor of future vaccination is prior vaccination, and these studies assessed participants in aggregate (<xref rid="R6" ref-type="bibr"><italic>6</italic></xref>). Guided by the Health Belief Model (<xref rid="R10" ref-type="bibr"><italic>10</italic></xref>), we investigated whether experiences with prior vaccination might affect the effectiveness of certain communication strategies (<xref ref-type="local-data" rid="SD1">Appendix</xref>).</p><p>Our study is a secondary analysis of a randomized experiment to test communication strategies and their effects on influenza immunization (<xref rid="R6" ref-type="bibr"><italic>6</italic></xref>&#x02013;<xref rid="R9" ref-type="bibr"><italic>9</italic></xref>). After our study was deemed exempt from review by the University of Michigan Institutional Review Board, we recruited a stratified random sample of adults from a panel of Internet users through Survey Sampling International (<ext-link ext-link-type="uri" xlink:href="https://www.surveysampling.com">https://www.surveysampling.com</ext-link>) (<xref ref-type="local-data" rid="SD1">Appendix</xref>). We recruited participants from 11 countries: Finland (n = 1,554), Norway (n = 764), Sweden (n = 1,539), Hungary (n = 998), Poland (n = 1,509), Spain (n = 1,604), Italy (n = 1,509), Germany (n = 1,546), the Netherlands (n = 1,938), the United Kingdom (n = 1,762), and the United States (n = 1,787).</p><p>Participants read a hypothetical news article that described the spread of influenza in their country. The article directly quoted hypothetical health experts and contained information about the influenza virus, its potential symptoms, and a vaccine in development. Articles were cross-randomized to provide participants with 5 varying communication strategies: 1) graphics (heat map, DOT map, picto-trendline) (<xref rid="R6" ref-type="bibr"><italic>6</italic></xref>); 2) case severity (severe, typical, both) (<xref rid="R9" ref-type="bibr"><italic>9</italic></xref>); confident language (scientific certainty, uncertainty, uncertainty with normalizing language) (<xref rid="R7" ref-type="bibr"><italic>7</italic></xref>); 4) influenza label (H11N3 influenza, horse flu, Yarraman flu) (<xref rid="R8" ref-type="bibr"><italic>8</italic></xref>); and 5) metaphor use (infectious disease, war, gardening). The Appendix contains more information about communication strategies. Each news article contained all 5 communication strategies. The experiment used a 3 &#x000d7; 3 &#x000d7; 3 &#x000d7; 3 &#x000d7; 3 between-subjects factorial design in which participants were randomly assigned to each communication strategy. After reading the newspaper article, participants were asked their vaccination status (whether they had received an influenza vaccination within the past 2 years) and intent to get vaccinated in the future (defined by a discrete visual analog scale ranging from 1 [&#x0201c;Definitely would not get a vaccination&#x0201d;] to 7 [&#x0201c;Definitely would get a vaccination&#x0201d;]).</p><p>We were interested in the main effect for an individual communication strategy depending on a participant&#x02019;s prior vaccination status. For each communication strategy, we conducted separate ordinal logistic regression models and included an interaction term of prior vaccination and the communication strategy of interest for each model. The dependent variable was intent to get vaccinated. As covariates, we included the participant&#x02019;s age, sex, and marital status and whether the participant was a healthcare worker. We estimated robust SEs with clustering by the participant&#x02019;s country of residence.</p><p>Of 20,138 participants, 16,401 (81%) completed the survey; of these, 4,999 (30%) had received an influenza vaccination within the previous 2 years and 11,402 (70%) had not. The average age was 51.4 (SD &#x000b1; 16.9) for vaccinated and 44.9 (SD &#x000b1; 15.4) for nonvaccinated participants. Approximately 44.6% of vaccinated and 52.1% of nonvaccinated participants were female (<xref ref-type="local-data" rid="SD1">Appendix</xref> Table 1).</p><p>Our results showed that previously vaccinated participants were more likely than nonvaccinated participants to plan for future vaccinations (adjusted odds ratio 5.8, 95% CI 4.8&#x02013;7.0; p&#x0003c;0.001). We found significant interaction effects between prior vaccination and each communication strategy (p&#x0003c;0.001 for each strategy) (<xref rid="T1" ref-type="table">Table;</xref>
<xref ref-type="local-data" rid="SD1">Appendix</xref> Table 2). However, this effect varied according to the type of communication strategy. Nonvaccinated participants reported greater intent for future vaccination when heat maps, severe cases, confident language, or exotic influenza labels were used (<xref rid="T1" ref-type="table">Table</xref>). Vaccinated participants reported greater intent for future vaccination when confident language or scientific/exotic influenza labels were used (<xref rid="T1" ref-type="table">Table</xref>). The use of metaphors had no effect on either group.</p><table-wrap id="T1" position="float"><label>Table</label><caption><title>Effect of communication strategies on intent for future influenza vaccination, by influenza vaccination status*</title></caption><table frame="hsides" rules="groups"><col width="157" span="1"/><col width="60" span="1"/><col width="60" span="1"/><col width="60" span="1"/><col width="60" span="1"/><col width="81" span="1"/><thead><tr><th rowspan="2" valign="bottom" align="left" scope="col" colspan="1">Strategy<hr/></th><th valign="bottom" colspan="4" align="center" scope="colgroup" rowspan="1">Vaccination over previous 2 y, adjusted odds ratio (95% CI)*</th><th rowspan="2" valign="bottom" align="center" scope="col" colspan="1">p value for interaction&#x02020;<hr/></th></tr></thead><tbody><tr><td valign="bottom" colspan="1" align="center" scope="row" rowspan="1">No<hr/></td><td valign="bottom" align="center" rowspan="1" colspan="1">p value<hr/></td><td valign="bottom" align="center" rowspan="1" colspan="1">Yes<hr/></td><td valign="bottom" align="center" rowspan="1" colspan="1">p value<hr/></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Graph type</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="center" rowspan="1" colspan="1">&#x0003c;0.001</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1"> Picto-trendline</td><td valign="top" align="center" rowspan="1" colspan="1">Referent</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="center" rowspan="1" colspan="1">Referent</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1"> DOT map</td><td valign="top" align="center" rowspan="1" colspan="1">1.1 (0.9&#x02013;1.2)</td><td valign="top" align="center" rowspan="1" colspan="1">0.06</td><td valign="top" align="center" rowspan="1" colspan="1">1.0 (0.9&#x02013;1.1)</td><td valign="top" align="center" rowspan="1" colspan="1">0.92</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1"> Heat map<hr/></td><td valign="top" align="center" rowspan="1" colspan="1">1.1 (1.0&#x02013;1.2)<hr/></td><td valign="top" align="center" rowspan="1" colspan="1">0.01<hr/></td><td valign="top" align="center" rowspan="1" colspan="1">1.1 (0.9&#x02013;1.2)<hr/></td><td valign="top" align="center" rowspan="1" colspan="1">0.08<hr/></td><td valign="top" align="left" rowspan="1" colspan="1"><hr/></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Case severity</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="center" rowspan="1" colspan="1">&#x0003c;0.001</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1"> Both</td><td valign="top" align="center" rowspan="1" colspan="1">Referent</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="center" rowspan="1" colspan="1">Referent</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1"> Typical</td><td valign="top" align="center" rowspan="1" colspan="1">1.0 (0.9&#x02013;1.1)</td><td valign="top" align="center" rowspan="1" colspan="1">0.78</td><td valign="top" align="center" rowspan="1" colspan="1">0.9 (0.8&#x02013;1.0)</td><td valign="top" align="center" rowspan="1" colspan="1">0.07</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1"> Severe<hr/></td><td valign="top" align="center" rowspan="1" colspan="1">1.1 (1.0&#x02013;1.3)<hr/></td><td valign="top" align="center" rowspan="1" colspan="1">0.02<hr/></td><td valign="top" align="center" rowspan="1" colspan="1">1.1 (0.9&#x02013;1.2)<hr/></td><td valign="top" align="center" rowspan="1" colspan="1">0.43<hr/></td><td valign="top" align="left" rowspan="1" colspan="1"><hr/></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Confident language</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="center" rowspan="1" colspan="1">&#x0003c;0.001</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1"> Uncertainty with normalizing language</td><td valign="top" align="center" rowspan="1" colspan="1">Referent</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="center" rowspan="1" colspan="1">Referent</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1"> Uncertainty</td><td valign="top" align="center" rowspan="1" colspan="1">1.0 (0.9&#x02013;1.1)</td><td valign="top" align="center" rowspan="1" colspan="1">0.97</td><td valign="top" align="center" rowspan="1" colspan="1">1.1 (0.9&#x02013;1.2)</td><td valign="top" align="center" rowspan="1" colspan="1">0.12</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1"> Scientific certainty<hr/></td><td valign="top" align="center" rowspan="1" colspan="1">1.2 (1.1&#x02013;1.3)<hr/></td><td valign="top" align="center" rowspan="1" colspan="1">&#x0003c;0.001<hr/></td><td valign="top" align="center" rowspan="1" colspan="1">1.3 (1.1&#x02013;1.4)<hr/></td><td valign="top" align="center" rowspan="1" colspan="1">&#x0003c;0.001<hr/></td><td valign="top" align="left" rowspan="1" colspan="1"><hr/></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Influenza label</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="center" rowspan="1" colspan="1">&#x0003c;0.001</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1"> Horse</td><td valign="top" align="center" rowspan="1" colspan="1">Referent</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="center" rowspan="1" colspan="1">Referent</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1"> H11N3</td><td valign="top" align="center" rowspan="1" colspan="1">1.0 (0.9&#x02013;1.1)</td><td valign="top" align="center" rowspan="1" colspan="1">0.62</td><td valign="top" align="center" rowspan="1" colspan="1">1.4 (1.1&#x02013;1.7)</td><td valign="top" align="center" rowspan="1" colspan="1">0.001</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1"> Yarraman<hr/></td><td valign="top" align="center" rowspan="1" colspan="1">1.1 (1.0&#x02013;1.2)<hr/></td><td valign="top" align="center" rowspan="1" colspan="1">0.001<hr/></td><td valign="top" align="center" rowspan="1" colspan="1">1.2 (1.1&#x02013;1.4)<hr/></td><td valign="top" align="center" rowspan="1" colspan="1">0.001<hr/></td><td valign="top" align="left" rowspan="1" colspan="1"><hr/></td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1">Metaphor use</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="center" rowspan="1" colspan="1">&#x0003c;0.001</td></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1"> Infectious disease</td><td valign="top" align="center" rowspan="1" colspan="1">Referent</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="center" rowspan="1" colspan="1">Referent</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1"> War</td><td valign="top" align="center" rowspan="1" colspan="1">1.0 (0.9&#x02013;1.1)</td><td valign="top" align="center" rowspan="1" colspan="1">0.78</td><td valign="top" align="center" rowspan="1" colspan="1">1.0 (0.9&#x02013;1.1)</td><td valign="top" align="center" rowspan="1" colspan="1">0.60</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" scope="row" rowspan="1" colspan="1"> Gardening</td><td valign="top" align="center" rowspan="1" colspan="1">1.0 (0.9&#x02013;1.1)</td><td valign="top" align="center" rowspan="1" colspan="1">0.75</td><td valign="top" align="center" rowspan="1" colspan="1">1.0 (0.9&#x02013;1.1)</td><td valign="top" align="center" rowspan="1" colspan="1">0.41</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr></tbody></table><table-wrap-foot><p>*Multivariable ordinal logistic regression adjusted for participant age, sex, marital status, occupation as healthcare worker, and country of residence.&#x02028;&#x02020;Interaction between vaccination status and communication strategy.</p></table-wrap-foot></table-wrap><p>This study should be interpreted in the context of certain limitations. For instance, participants reviewed a hypothetical news article, which may be different than direct communication with a healthcare provider or reading an actual article during a pandemic.</p><p>Certain communication strategies, such as use of confident language or an exotic influenza label, were effective regardless of prior vaccination status. Yet use of a scientific influenza label was more effective than use of an exotic influenza label among previously vaccinated participants. Other communication strategies, such as use of heat maps or describing severe cases, were effective among nonvaccinated but not previously vaccinated participants. Vaccination rates for influenza may be improved by targeting healthcare communication based on prior vaccination experiences (<xref rid="R11" ref-type="bibr"><italic>11</italic></xref>,<xref rid="R12" ref-type="bibr"><italic>12</italic></xref>).</p><supplementary-material content-type="local-data" id="SD1"><caption><title>Appendix</title><p>Additional methods and results for study of prior vaccination and effectiveness of communication strategies used to describe infectious diseases.</p></caption><media mimetype="application" mime-subtype="pdf" xlink:href="17-1408-Techapp-s1.pdf" xlink:type="simple" id="d35e523" position="anchor"/></supplementary-material></body><back><fn-group><fn fn-type="citation"><p><italic>Suggested citation for this article</italic>: Valley TS, Scherer AM, Knaus M, Zikmund-Fisher BJ, Das E, Fagerlin A. Prior vaccination and effectiveness of communication strategies used to describe infectious diseases. Emerg Infect Dis. 2019 Apr [<italic>date cited</italic>]. <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3201/eid2504.171408">https://doi.org/10.3201/eid2504.171408</ext-link></p></fn></fn-group><ack><title>Acknowledgments</title><p>This work was supported by the European Union&#x02019;s Seventh Framework Programme for research, technological development and demonstration under grant agreement #278763 (to A.F.) and the National Institutes of Health, K23HL140165 (to T.S.V.). Funding sources had no role in the study conception, design, conduct, analysis, or manuscript construction.</p></ack><ack><title>About the Author</title></ack><bio id="d35e544"><p>Dr. Valley is an assistant professor in the Division of Pulmonary and Critical Care Medicine at the University of Michigan. 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