Information Sources and Vaccination in the COVID-19 Pandemic

Information Sources and Vaccination in the COVID-19 Pandemic | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Information Sources and Vaccination in the COVID-19 Pandemic Nana Osei Asiamah, Paige Miller, Xiaoxu Yang, Wesley Shrum This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4288648/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Oct, 2024 Read the published version in Discover Public Health → Version 1 posted 9 You are reading this latest preprint version Abstract Among the issues that remained contentious throughout the pandemic was vaccination: its efficacy, side effects, and the general reluctance of a substantial segment of the population to get vaccinated. The aim of this paper is to understand the role of health information sources in anti-vaccination sentiment and the decision to vaccinate. Linear and logistic regression models were used to analyze data from an online survey of adults in the United States in late 2021 (n=10,221). The results of the study showed that: (a) information from local and national health experts had a significant positive association with getting the COVID-19 vaccine and a negative relationship with holding anti-vaccination sentiments while (b) information from social media and community/religious leaders had the opposite effect. Overall, this study highlights the importance of public health systems in the dissemination of information on vaccinations during pandemics. vaccines anti-vaccination information sources COVID-19 Introduction While COVID-19 infection and response were global phenomena, few countries experienced more controversy over precautionary actions than the U.S. [ 1 ]. Among the issues that remained contentious throughout the pandemic was vaccination: its efficacy, side effects, and the general reluctance of a substantial segment of the population to get vaccinated [ 2 ]. Some degree of vaccine hesitancy was present worldwide, but the United States experienced polarized discourse centered around both unvaccinated persons and anti-vaccination conspiracy theories. Vaccine hesitancy is, of course, not limited to COVID-19. For instance, both African Americans and Hispanics were more likely to get a swine flu vaccine than Whites in the US [ 3 ] while Whites and Asians were more likely to get vaccinated in the UK [ 4 ]. Earlier studies have analyzed the socio-demography of the unvaccinated as well as their political leanings [ 5 – 6 ]. Racial minority status and female gender were reported to be associated with vaccine hesitancy, although they have different effects [ 7 ]. Additionally, having children and lower educational attainment were related to vaccine hesitance [ 8 – 10 ]. A recent Canadian study showed that individuals who identified as non-white, South Asian, or had indigenous ancestry were less likely to get a COVID-19 vaccine [ 11 ]. Specific to COVID-19, studies have sought to understand the correlates of anti-vaccination conspiracies [ 12 – 13 ]. Conservatives and moderates have been found to be more likely to hold anti-vaccination sentiments about pre-COVID-19 vaccines [ 14 ]. Anti-vaccination sentiments are also not new in the U.S. context. These notions have been present in various vaccination campaigns such as smallpox and human papillomavirus vaccines (HPV) [ 15 ]. Some literature identifies different types of anti-vaccination attitudes. Martin and Petrie lay out four variations, including mistrust of its benefits, concern about side effects, preference for natural immunity, and the profit motivations of pharmaceutical companies [ 16 ]. These attitudes are associated with vaccine intentions [ 17 ]. The most widely cited theories that provide a framework for understanding individuals’ health behavior are the theory of planned behavior and the health belief model [ 18 ]. The former proposes that behavior should be analyzed through the ‘plan’ to perform the behavior [ 19 ] while the latter posits “that messages will achieve optimal behavior change if they successfully target perceived barriers, benefits, self-efficacy, and threat” [ 20 ]. An important aspect of any public health campaign is the dissemination of information. During the COVID-19 pandemic, people relied on a wide range of sources to understand the unfolding events and held a diversity of opinions as to which sources of information were reliable. Trent and colleagues found that an important predictor of vaccine acceptance was an individual’s trust in information from national governments and health experts [ 21 ]. Another study showed that trust in sources of information that promote a positive vaccine narrative was associated with more favorable dispositions to vaccinate [ 22 ]. There have also been studies on the association between one’s perception of the reliability of an information source and views on vaccination [ 23 – 24 ]. The present study seeks to investigate the relationship between the information sources that were considered reliable and two dimensions of behavior and belief: (1) COVID-19 vaccination and (2) anti-vaccination sentiments. Based on the existing literature, we hypothesize that (a) those who considered information from traditional sources to be reliable (e.g., health experts, government, and traditional media) would be more likely to get vaccinated and less likely to hold anti-vaccination sentiments, and (b) those who viewed sources such as social media and religious organizations as reliable would be less likely to vaccinate and more likely to hold anti-vaccination sentiments. Methods Data and sample Our analysis is based on online surveys gathered between August 10–22, 2021. This period corresponds with the point at which the Delta wave of the pandemic was ending, and the Omicron wave was about to begin. The Alchemer software platform was used to invite 25,098 distinct respondents in the United States to complete an Internet-based survey [1] . A total of 10,022 cases remained after (a) eliminating incomplete surveys and (b) disqualifying respondents who answered too late or failed to correctly answer such screening questions as “check the ‘red’ option below”. The University of Wisconsin (River Falls) Human Research Ethics Committee reviewed and approved the study protocol on human subjects’ research. All respondents were informed about the purpose of the study and were required to provide electronic consent before the study commenced. Responses were anonymized. The non-probability quota sample was designed to closely match the distribution of the US population by race/ethnicity, sex, age, and geographic location (South, West, Northeast, and Mid-West). African Americans were specifically targeted to ensure that the sample matched the US Census proportion of the population. Data for the independent and dependent variables were obtained from 98% of respondents. Measures Table 1 Descriptive information on scales Variable Item wording/index statistics Response categories (no. of points) ANTIVAX scale Mean = 9.50; standard error = 3.94; Cronbach's = 0.80 5–20 VRISK I have sometimes felt I'd rather take the risk of getting sick than get the vaccine. Strongly disagree (1)- Strongly agree (4) VSIDE I have worried the vaccine may be harmful or have side effects. Strongly disagree (1)- Strongly agree (4) VCOST I have worried about the financial cost associated with receiving the COVID-19 vaccine. Strongly disagree (1)- Strongly agree (4) VNONEED I have already had COVID-19 so I don't believe a vaccine is necessary for me. Strongly disagree (1)- Strongly agree (4) VWHICH With multiple vaccines available, I have been concerned about which one is best for me. Strongly disagree (1)- Strongly agree (4) CONSPIRE scale Mean = 14.13; standard error = 6.20; Cronbach's = 0.91 7–28 BIOWEAPO Coronavirus is a bioweapon developed by a government or terrorist organization Strongly disagree (1)- Strongly agree (4) BIGPHARM Coronavirus was developed by pharmaceutical industries that will generate large profits Strongly disagree (1)- Strongly agree (4) REDUCPOP Coronavirus was designed to reduce the world population Strongly disagree (1)- Strongly agree (4) LABVIRUS Chinese created the coronavirus in a laboratory for its political gain Strongly disagree (1)- Strongly agree (4) CELLTOW Coronavirus has been linked to 5G cellphone towers Strongly disagree (1)- Strongly agree (4) DIVINE What has happened is all part of a divine plan Strongly disagree (1)- Strongly agree (4) RESTRICT The government is using coronavirus restrictions to see how much control they can exert over people's behavior Strongly disagree (1)- Strongly agree (4) [Table 1 about here.] While the data utilized are cross-sectional and do not permit causal inferences, we focus on associations with information sources with two dependent dimensions: (1) having been vaccinated at least once and (2) anti-vaccination sentiment . The former is a binary categorical variable based on the question: “Have you had at least one vaccine for the coronavirus?” The latter is a scale created to measure anti-vaccination sentiments culled from both conventional and new media as well as other surveys fielded during the pandemic. These include a diversity of reasons for not taking the vaccine such as fear of side effects, financial costs, and already having had COVID-19. Table 1 shows scale items and reliability coefficients. Table 2 Descriptive statistics for study variables Mean/% SD Lived in Community setting 10.6% 0.31 Female 54.2% 0.50 Black 10.2% 0.30 Hispanic 3.1% 0.17 Asian 3.1% 0.17 Native American 0.8% 0.09 Age 45 1.83 College or more 46.7% 0.50 No of children 1.44 0.49 Attend Religious Service 67.0% 1.15 Employed 48.0% 0.50 Left 23.5% 0.74 Moderate 44.7% 0.74 Right 31.7% 0.74 Income > $ 50,000 51.0% 0.50 Income < $ 50,000 48.0% 0.50 Had at least one vaccine (yes) 73.0% 0.44 Diagnosed with Covid 19 13.0% 0.34 Conspire 14.1 6.20 Anti -Vax Scale 9.5 0.04 Reliability of Information Source Regulated Sources (%very reliable) Local Health experts 29.7% 0.86 National Health experts (like the CDC) 36.9% 1.00 Local Government 21.2% 0.93 National Government 23.9% 0.99 Less Regulated Sources (%very reliable) Community or Religious leaders 12.7% 0.99 Social media such as Facebook/Twitter 9.7% 0.98 Journalists (Broadcast/Print/Online) 17.1% 0.97 n = 10,022 [Table 2 about here.] Demographically, sample proportions are largely similar to the US population over age 18. As shown in Table 2 , 54% of the 10,022 participants were female, 10% identified as African American, almost half had at least a college degree, about half the sample earned $ 50,000 or more annually, 48% were employed, and one in ten lived in a community setting. [2] Two-thirds of the sample attended religious services at least once. While 24% of our sample identified as politically left-leaning, 32% identified as right-leaning. Since the survey was conducted at the end of the Delta wave, just as the Omicron wave was beginning, we included a binary variable that measured whether one had been diagnosed with COVID-19 by a health professional (13% at the time of the survey). To control for the possibility that conspiracy beliefs might affect the relationship between information sources and vaccination, we included a conspiracy scale measured by summing seven items (range: 7–28). Respondents were prompted with, “There are many views about the coronavirus episode. To what extent do you agree or disagree with the following?” They were presented with statements like “China created the coronavirus in a laboratory for its political gain,” and “The government is using coronavirus restrictions to see how much control they can exert over people”. The complete list of conspiracy scale items may be found in Table 1 . Our primary independent variables measured usage and perceptions of reliability for seven different information sources about COVID-19. We view information sources as divided into two broad categories, depending on the degree to which organizational or professional guidelines are generally viewed as constraining message content. More regulated sources include local health experts, national health experts (like the CDC), local government, and national government. Less regulated sources include social media such as Facebook and Twitter, as well as religious or community leaders, and journalists (broadcast, print, or online). Their perceived reliability was measured as an ordinal variable with four response categories: (4) very reliable, (3) somewhat reliable, (2) rarely reliable, (1) never reliable. Analytic method Two regression analyses are used to assess the outcome variables owing to variation in their measurement format. We use logistic regression to examine predictors significantly associated with vaccination status. In the first model, we assess independent variables that are related to vaccination controlling for covariates. In the second, we include only those predictors that were statistically significant in the first model to establish changes in the magnitude or direction of the relationship net of controls. Next, we employ linear regression to assess the nature of the relationship between the independent variables and anti-vaccination sentiment. As mentioned above, the first model assesses the associations between the main predictor variables and anti-vaccination sentiment, controlling for covariates. The second model assesses the changes when only the statistically significant independent variables are used in the analysis. Both analyses highlight significant dimensions associated with the outcome variables, showing that information about COVID-19 from local health experts, community/religious leaders, and social media was significantly associated with both vaccination and anti-vaccination sentiments. Results Table 3 is available in the Supplementary Files section.</p [Table 3 about here.] Bivariate analyses on all the variables in the study were completed to assess the correlations between variables. There were significant correlations between the main independent variables and both outcome variables. The only exception was social media, which did not have a significant relationship with vaccination status. Additionally, while the relationship between vaccination status and the primary independent variables were all positive, social media had a negative effect. On the other hand, the correlation effects between anti-vaccination sentiments and the independent variables were negative with two exceptions. Information from religious leaders and social media (perceived as reliable) had a positive relationship with anti-vaccination sentiments. Table 4 Regression Analysis of Vaccination and Anti-Vaccination Sentiments Vaccinated Anti-vax Sentiments Model 1 Model 2 Model 3 Model 4 b b B b Lived in community setting 0.76 *** 0.80 *** 0.68 *** 0.68 *** Female -0.14 - -0.06 - African American -0.40 ** -0.39 ** -0.34 * -0.32 * Hispanic 0.37 - 0.00 - Asian 1.02 *** 1.00 *** -0.24 - Native American -0.58 - 0.17 - Age 0.37 *** 0.37 *** -0.57 *** -0.52 *** Highest educational level 0.50 *** 0.53 *** 0.01 - Number of Children -0.20 * -0.22 * 0.03 - Attend religious service 0.07 - 0.09 * 0.09 * Now employed 0.21 * 0.22 * 0.20 - Political Spectrum -0.21 ** -0.16 ** 0.35 *** 0.38 *** Annual household Income 0.50 *** 0.54 *** -0.23 * -0.16 Diagnosed with COVID-19 0.45 *** 0.44 *** 1.57 *** 1.61 *** Conspiracy Scale -0.08 *** -0.08 *** 0.32 *** 0.33 *** Reliability of Information More Regulated Sources Local Health experts 0.20 ** 0.21 ** -0.17 * -0.29 *** National health experts 0.42 *** 0.40 *** -0.12 - Local Government 0.25 *** 0.22 ** -0.10 - National Government 0.15 * 0.12 -0.06 - Less Regulated Sources Community & Religious leaders -0.12 * -0.10 * 0.23 *** 0.20 *** Social Media -0.13 * -0.16 ** 0.27 *** 0.27 *** Journalists -0.08 - 0.24 * 0.17 ** Note: *p < 0.05, **p < 0.01, ***p < 0.001 Model 1 Count R 2 = 0.808 Model 2 Count R 2 = 0.803 Model 3 Adjusted R 2 = 0.565 Model 4 Adjusted R 2 = 0.559 [Table 4 about here.] Regression analyses for both outcomes are shown in Table 4 . The impact of all independent dimensions on vaccination status and anti-vaccination sentiment is shown in Models 1 and 3, while the reduced Models 2 and 4 contain only significant predictors. Tests for multicollinearity in the predictors and the anti-vaccination sentiments model were conducted by examining Variance Inflation Factors and Tolerance Values. The results show that all predictors had a VIF less than 4 and tolerance values greater than 0.2, indicating no issues with multicollinearity. Several demographic and control variables are significant and positively related to both vaccination status and anti-vaccination sentiments. Gender and Native American or Hispanic status were not significant in any model we tested. Model 2 shows that older, employed individuals with higher educational attainment and household income were more likely to be vaccinated, as were Asians and those who lived in communal settings. Having been diagnosed with COVID-19 was also associated with an increased likelihood of vaccination. African Americans, those who lean politically to the right, those who have children, and those with higher conspiracy scores were less likely to be vaccinated. Anti-vaccination sentiments were higher for those who live in communal settings and attend religious service, as shown in Model 4. Those who leaned right politically, had been diagnosed with COVID-19, and had higher scores on the conspiracy scale were also more likely to hold anti-vaccination sentiments, whereas African Americans (compared with Whites) and older individuals were less likely to hold such opinions. Log odds coefficients for the perceived reliability of information sources are presented in the second part of Table 3 . Positive effects on vaccination (Model 2) were significant for (a) local health experts, (b) national health experts (like CDC), and (c) local government. Those who perceive social media and religious/community leaders as more reliable were less likely to be vaccinated. The predicted probability that a respondent received a vaccine if they trusted information from regulated sources increases by approximately 10% from lowest (never) to highest (very) values for all significant sources except national government, which increases by 20%. Anti-vaccination sentiments were relatively consistent with vaccination itself with respect to their association with information sources. Coefficients for the reliability of information sources were negative for local health experts but positive for social media, community/religious leaders, and journalists. In other words, those who viewed the information from local health experts as reliable were less likely to hold anti-vaccination views, while people who trusted information from social media, religious/community leaders, and other media were more likely to hold anti-vaccination sentiments, holding all other variables constant. Discussion Information plays a critical role in public health management. The success of the COVID-19 vaccination campaign cannot be uncoupled from the flow of information and public perceptions of source reliability. Before COVID-19, studies indicated that the crux of the vaccination issue may have been trust or distrust in governmental and health institutions [25]. During COVID-19, a 2021 study indicated that increased trust in health experts was an important predictor of receiving a vaccine [26], while a 2022 study suggested that information from regulated sources was associated with individual health choices [27]. The national survey analyzed here sought to examine specific information sources and their association with both vaccination and anti-vaccination beliefs. Information sources vary in the extent to which organizational constraints limit or encourage certain kinds of outputs, as well as the extent to which people view them as reliable. Such sources include both those that tend to be more regulated (e.g., health experts and government sources) and those that are less regulated (social media, religious and community leaders, and journalists). The results here show that information from regulated sources was positively associated with vaccination status while information from less regulated sources was negatively associated. Correspondingly, anti-vaccination sentiments were higher for those who viewed social media and community/religious leaders as reliable. Those who viewed local health experts as reliable were less likely to hold such sentiments. As Harper and colleagues suggest, this may be partially due to the more consistent communication of the threat posed by the virus by health and government experts [28]. The association between information from local health experts and anti-vaccination sentiments is important because it raises the issue of trust. Perhaps a plausible basis for this nexus is the relationship that exists between local experts and their communities. Respondents who viewed social media, community/religious leaders, and journalists as reliable may have received diverse or even contradictory information, resulting in a lower probability of vaccination and greater anti-vaccination sentiments. Sieber and colleagues suggested that the impact of social media was to reduce the likelihood that people get the COVID-19 vaccine [24]. Indeed, there may be a cascading effect. Once people begin to consume anti-vaccination materials they may seek and find more such content [29]. In a network analysis of 100 million users on Facebook, 4.2% held anti-vaccination views, 6.9% were in favor of vaccination and 74.1% were “undecided” about the topic [30]. Journalism may have a similar effect. Thus, people with distinct political leanings seek out journalists who share their political inclinations exposing people to a narrower range of information through their social media platforms. We note that information from national and local governments was not significantly associated with anti-vaccination sentiments. However, it must be noted that some government officials used social media platforms to communicate the need for COVID-19 measures. It is difficult to measure the impact these officials had on vaccination or vaccine hesitance because different platforms appeal to specific demographics. While several dimensions were related to vaccination but not anti-vaccination beliefs (education, income, children, and the reliability of local/national government), one interesting finding that emerges from this analysis are sources of inconsistency: factors related to behavior (vaccination) and belief (anti-vaccination) in unexpected ways. At the bivariate level, vaccinated respondents are less likely than those who are unvaccinated to hold any of the five beliefs in our anti-vaccination scale. Our conventional understanding is that behavior and attitudes tend toward consistency [3] [31]. Whether the causal direction is (a) from sentiment to behavior or (b) from behavior to sentiment, we typically expect vaccination to be negatively correlated with anti-vaccination sentiments. Consistency of the correlates of behavior and belief is implied if those factors positively associated with vaccination are negatively associated with anti-vaccination sentiment. In statistical terms, evidence for consistent predictors of behavior and belief would be coefficients in Models 2 and 4 that are reversed in sign: (a) factors positively associated with vaccination should be negatively associated with anti-vaccination sentiments, while (b) factors negatively associated with vaccination should be positively associated with anti-vaccination sentiments. Yet, as Table 3 shows, only one positive factor (age) is negatively associated with anti-vaccination sentiment, while only two negative factors (right-leaning politics and conspiracy beliefs) are positively associated with anti-vaccination beliefs. Inconsistency in predictors is common, beginning with race. African Americans were less likely to have had a vaccine, but also less likely to hold anti-vaccine sentiments. This finding supports prior work that found African Americans less willing to vaccinate than other races [3-4]. The literature on vaccine behavior and distrust of the government also shows the reluctance of African Americans to receive vaccines [32-33]. Further, some studies highlight the challenge some minority groups experience accessing vaccines in the nascent stage of the program [34]. However, they are less likely than Whites to hold anti-vaccination sentiments. Community living and having a positive COVID-19 status also had inconsistent relations with both outcome variables. Those who lived in community settings and had been diagnosed with COVID-19 were more likely than others to have been vaccinated but were also more likely to hold anti-vaccination sentiments. One possible explanation for this is that each of these factors was associated with social network pressures to vaccinate, while at the same time placing individuals in contact with others who had diverse beliefs. Many group living arrangements imposed vaccination mandates as a requirement to remain in these statuses. Hence, people who lived in group settings were more likely to have received a vaccine. Yet at the same time, they may have been in contact with individuals unhappy about such requirements and exposed to anti-vaccination sentiments. Lastly, being diagnosed with COVID-19 was also positively associated with receiving at least one vaccine and holding anti-vaccination sentiments. While the present data does not tell us whether vaccination occurred before or after COVID-19 diagnosis, one possible reason for this association was the belief that having COVID-19 itself is sufficient to acquire antibodies and mandated vaccination increased receptivity to antivaccination beliefs. Future studies should explore the reasons for these inconsistencies. This analysis has three limitations. First, the study was cross-sectional, such that causal implications remain speculative. Second, the large sample size can produce statistically significant relationships that are not always substantively significant. Finally, data were gathered at the end of 2021 and the findings cannot be generalized to the second half of the pandemic as it evolved over time. This study attempted to understand the relationships between both sociodemographic factors and COVID-19 information sources with vaccination behavior and anti-vaccination sentiments. Since regulated sources—government and health experts—are associated with increased vaccination and lower anti-vaccination sentiments, agents from these sources need to think carefully about their relationships with social media, community leaders, and journalists if they wish to amplify and mitigate casualties. Local government should also improve the funding of local public health communication in future pandemics. Declarations Funding This study was funded by the Tommy G. Thompson Center on Public Leadership. The authors have no competing interests to declare that are relevant to the content of this article. Data Availability The data will be publicly available immediately after the principal findings regarding vaccines, conspiracies, and Covid impacts have been disseminated. Consent Informed consent was obtained from all individual participants included in the study. Authorship Contribution N.A. and W.S. wrote the main manuscript text and P.M. collected the data. N.A. and X.Y. analyzed the data. All authors reviewed the manuscript. References Wang Y. and Liu Y. Multilevel determinants of COVID-19 vaccination hesitancy in the United States: A Rapid Systematic Review. Preventive Medicine Reports 2022;25 Sabahelzain, M.M. Hartigan-Go, K., Larson, H.J. The Politics of COVID-19 Vaccine Confidence.” Current Opinion in Immunology 2021; Volume 71 p. 92-96. Mesch, Gustavo S., and Kent P. Schwirian. Confidence in Government and Vaccination Willingness in the USA. 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Hancock, and Sandra Crouse Quinn. Determinants of Trust in the Flu Vaccine for African Americans and Whites. Social Science & Medicine 2017; 193:70–79. Jamison, A. M., Quinn S. C., and Freimuth V. S. You Don’t Trust a Government Vaccine: Narratives of Institutional Trust and Influenza Vaccination among African American and White Adults. Social Science & Medicine 2019; 221:87– 94. Abba-Aji M., Stuckler, D., Galea, S., Mckee, M. Ethic/racial minorities and migrants ‘access to COVID-19 Vaccines: A Systematic Review of Barriers and Facilitators. Journal of Migration and Health 2022; Vol. 5 100086. Footnotes The number of eligible respondents within the Alchemer survey pool at the time of our survey was 25,098. These are U.S. citizens who have volunteered to take surveys in exchange for a small fee, similar to the Qualtrics survey platform. The first individuals who were eligible and completed the survey were our respondents. Others would not have had the opportunity, though they might have responded to the invitation, because they were too late. Quotas for gender, age, and so forth were established to represent the U.S. population, as described. Community living was left to the respondent’s interpretation. Generally, this is a type of residential facility like a nursing home or assisted living setting, but respondents may also have considered other arrangements as community living. This assumption does not always apply in the public health literature. Additional Declarations No competing interests reported. Supplementary Files Table3.docx Cite Share Download PDF Status: Published Journal Publication published 16 Oct, 2024 Read the published version in Discover Public Health → Version 1 posted Editorial decision: Revision requested 02 Aug, 2024 Reviews received at journal 16 Jul, 2024 Reviewers agreed at journal 09 Jul, 2024 Reviews received at journal 18 May, 2024 Reviewers agreed at journal 10 May, 2024 Reviewers invited by journal 10 May, 2024 Editor assigned by journal 06 May, 2024 Submission checks completed at journal 06 May, 2024 First submitted to journal 18 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4288648","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":300600833,"identity":"18aa597d-69aa-4606-a553-a6efbc3d16ea","order_by":0,"name":"Nana Osei Asiamah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYJCCAwwMNnJ8YCYb8VrSjNlI0gIEhxPbiNZizn784YGfOczpbfxnDBg+lB0mrMWyJ8fgYO82ttw2iRwDxhnniNBicCCH4QDvNh6gFh4DZt42YrScf/7g4N9tEulsQIcx/yVKy40Eg8O82wwS2BhyDJgZidPyxuCw7LYEwzaJtIKDPefSiXFY+uOPb7f9l+fnP7zxwY8ya8JaUMABEtWPglEwCkbBKMAFADlSOwE2ti+KAAAAAElFTkSuQmCC","orcid":"","institution":"Louisiana State University","correspondingAuthor":true,"prefix":"","firstName":"Nana","middleName":"Osei","lastName":"Asiamah","suffix":""},{"id":300600837,"identity":"ae4481d9-ac5e-4995-a383-c089b024aaa9","order_by":1,"name":"Paige Miller","email":"","orcid":"","institution":"University of Wisconsin–River Falls","correspondingAuthor":false,"prefix":"","firstName":"Paige","middleName":"","lastName":"Miller","suffix":""},{"id":300600841,"identity":"298c94d8-d58e-4229-819b-2e2ad05a825b","order_by":2,"name":"Xiaoxu Yang","email":"","orcid":"","institution":"Louisiana State University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxu","middleName":"","lastName":"Yang","suffix":""},{"id":300600843,"identity":"e3a28e46-7093-4e02-a4c2-bd0e01de07a1","order_by":3,"name":"Wesley Shrum","email":"","orcid":"","institution":"Louisiana State University","correspondingAuthor":false,"prefix":"","firstName":"Wesley","middleName":"","lastName":"Shrum","suffix":""}],"badges":[],"createdAt":"2024-04-18 15:06:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4288648/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4288648/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12982-024-00266-y","type":"published","date":"2024-10-16T15:57:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67148839,"identity":"0dd2b866-e624-4d90-9247-b97371a5433e","added_by":"auto","created_at":"2024-10-21 16:08:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":559687,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4288648/v1/9ae09580-8190-4d9b-b1c2-6075065bcc59.pdf"},{"id":56256677,"identity":"91d7f573-fbdf-46e4-af48-f4864bc885c5","added_by":"auto","created_at":"2024-05-10 13:56:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33543,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4288648/v1/8c9f3286b11e3debb58c867f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Information Sources and Vaccination in the COVID-19 Pandemic","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWhile COVID-19 infection and response were global phenomena, few countries experienced more controversy over precautionary actions than the U.S. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among the issues that remained contentious throughout the pandemic was vaccination: its efficacy, side effects, and the general reluctance of a substantial segment of the population to get vaccinated [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Some degree of vaccine hesitancy was present worldwide, but the United States experienced polarized discourse centered around both unvaccinated persons and anti-vaccination conspiracy theories.\u003c/p\u003e \u003cp\u003eVaccine hesitancy is, of course, not limited to COVID-19. For instance, both African Americans and Hispanics were more likely to get a swine flu vaccine than Whites in the US [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] while Whites and Asians were more likely to get vaccinated in the UK [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Earlier studies have analyzed the socio-demography of the unvaccinated as well as their political leanings [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Racial minority status and female gender were reported to be associated with vaccine hesitancy, although they have different effects [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, having children and lower educational attainment were related to vaccine hesitance [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A recent Canadian study showed that individuals who identified as non-white, South Asian, or had indigenous ancestry were less likely to get a COVID-19 vaccine [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Specific to COVID-19, studies have sought to understand the correlates of anti-vaccination conspiracies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Conservatives and moderates have been found to be more likely to hold anti-vaccination sentiments about pre-COVID-19 vaccines [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnti-vaccination sentiments are also not new in the U.S. context. These notions have been present in various vaccination campaigns such as smallpox and human papillomavirus vaccines (HPV) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Some literature identifies different types of anti-vaccination attitudes. Martin and Petrie lay out four variations, including mistrust of its benefits, concern about side effects, preference for natural immunity, and the profit motivations of pharmaceutical companies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These attitudes are associated with vaccine intentions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The most widely cited theories that provide a framework for understanding individuals\u0026rsquo; health behavior are the theory of planned behavior and the health belief model [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The former proposes that behavior should be analyzed through the \u0026lsquo;plan\u0026rsquo; to perform the behavior [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] while the latter posits \u0026ldquo;that messages will achieve optimal behavior change if they successfully target perceived barriers, benefits, self-efficacy, and threat\u0026rdquo; [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn important aspect of any public health campaign is the dissemination of information. During the COVID-19 pandemic, people relied on a wide range of sources to understand the unfolding events and held a diversity of opinions as to which sources of information were reliable. Trent and colleagues found that an important predictor of vaccine acceptance was an individual\u0026rsquo;s trust in information from national governments and health experts [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Another study showed that trust in sources of information that promote a positive vaccine narrative was associated with more favorable dispositions to vaccinate [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere have also been studies on the association between one\u0026rsquo;s perception of the reliability of an information source and views on vaccination [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study seeks to investigate the relationship between the information sources that were considered reliable and two dimensions of behavior and belief: (1) COVID-19 vaccination and (2) anti-vaccination sentiments. Based on the existing literature, we hypothesize that (a) those who considered information from traditional sources to be reliable (e.g., health experts, government, and traditional media) would be more likely to get vaccinated and less likely to hold anti-vaccination sentiments, and (b) those who viewed sources such as social media and religious organizations as reliable would be less likely to vaccinate and more likely to hold anti-vaccination sentiments.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eData and sample\u003c/h2\u003e\n \u003cp\u003eOur analysis is based on online surveys gathered between August 10\u0026ndash;22, 2021. This period corresponds with the point at which the Delta wave of the pandemic was ending, and the Omicron wave was about to begin. The Alchemer software platform was used to invite 25,098 distinct respondents in the United States to complete an Internet-based survey\u003csup\u003e[1]\u003c/sup\u003e. A total of 10,022 cases remained after (a) eliminating incomplete surveys and (b) disqualifying respondents who answered too late or failed to correctly answer such screening questions as \u0026ldquo;check the \u0026lsquo;red\u0026rsquo; option below\u0026rdquo;. The University of Wisconsin (River Falls) Human Research Ethics Committee reviewed and approved the study protocol on human subjects\u0026rsquo; research. All respondents were informed about the purpose of the study and were required to provide electronic consent before the study commenced. Responses were anonymized. The non-probability quota sample was designed to closely match the distribution of the US population by race/ethnicity, sex, age, and geographic location (South, West, Northeast, and Mid-West). African Americans were specifically targeted to ensure that the sample matched the US Census proportion of the population. Data for the independent and dependent variables were obtained from 98% of respondents.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eMeasures\u003c/h2\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive information on scales\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItem wording/index statistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResponse categories (no. of points)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eANTIVAX scale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;=\u0026thinsp;9.50; standard error\u0026thinsp;=\u0026thinsp;3.94; Cronbach\u0026apos;s\u0026thinsp;=\u0026thinsp;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026ndash;20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVRISK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI have sometimes felt I\u0026apos;d rather take the risk of getting sick than get the vaccine.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrongly disagree (1)- Strongly agree (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVSIDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI have worried the vaccine may be harmful or have side effects.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrongly disagree (1)- Strongly agree (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVCOST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI have worried about the financial cost associated with receiving the COVID-19 vaccine.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrongly disagree (1)- Strongly agree (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVNONEED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI have already had COVID-19 so I don\u0026apos;t believe a vaccine is necessary for me.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrongly disagree (1)- Strongly agree (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVWHICH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith multiple vaccines available, I have been concerned about which one is best for me.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrongly disagree (1)- Strongly agree (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCONSPIRE scale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;=\u0026thinsp;14.13; standard error\u0026thinsp;=\u0026thinsp;6.20; Cronbach\u0026apos;s\u0026thinsp;=\u0026thinsp;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u0026ndash;28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBIOWEAPO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoronavirus is a bioweapon developed by a government or terrorist organization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrongly disagree (1)- Strongly agree (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBIGPHARM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoronavirus was developed by pharmaceutical industries that will generate large profits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrongly disagree (1)- Strongly agree (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREDUCPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoronavirus was designed to reduce the world population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrongly disagree (1)- Strongly agree (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLABVIRUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChinese created the coronavirus in a laboratory for its political gain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrongly disagree (1)- Strongly agree (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCELLTOW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoronavirus has been linked to 5G cellphone towers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrongly disagree (1)- Strongly agree (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDIVINE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhat has happened is all part of a divine plan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrongly disagree (1)- Strongly agree (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRESTRICT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe government is using coronavirus restrictions to see how much control they can exert over people\u0026apos;s behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrongly disagree (1)- Strongly agree (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e[Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e about here.]\u003c/p\u003e\n \u003cp\u003eWhile the data utilized are cross-sectional and do not permit causal inferences, we focus on associations with information sources with two dependent dimensions: (1) \u003cem\u003ehaving been vaccinated at least once\u003c/em\u003e and (2) \u003cem\u003eanti-vaccination sentiment\u003c/em\u003e. The former is a binary categorical variable based on the question: \u0026ldquo;Have you had at least one vaccine for the coronavirus?\u0026rdquo; The latter is a scale created to measure anti-vaccination sentiments culled from both conventional and new media as well as other surveys fielded during the pandemic. These include a diversity of reasons for not taking the vaccine such as fear of side effects, financial costs, and already having had COVID-19. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows scale items and reliability coefficients.\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics for study variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean/%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLived in Community setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNative American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo of children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttend Religious Service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome \u0026gt; \u003cspan\u003e$\u003c/span\u003e50,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome \u0026lt; \u003cspan\u003e$\u003c/span\u003e50,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHad at least one vaccine (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosed with Covid 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConspire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti -Vax Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eReliability of Information Source\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulated Sources\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(%very reliable)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocal Health experts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNational Health experts (like the CDC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocal Government\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNational Government\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLess Regulated Sources\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(%very reliable)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity or Religious leaders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial media such as Facebook/Twitter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJournalists (Broadcast/Print/Online)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;10,022\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e[Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e about here.]\u003c/p\u003e\n \u003cp\u003eDemographically, sample proportions are largely similar to the US population over age 18. As shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, 54% of the 10,022 participants were female, 10% identified as African American, almost half had at least a college degree, about half the sample earned \u003cspan\u003e$\u003c/span\u003e50,000 or more annually, 48% were employed, and one in ten lived in a community setting.\u003csup\u003e[2]\u003c/sup\u003e Two-thirds of the sample attended religious services at least once. While 24% of our sample identified as politically left-leaning, 32% identified as right-leaning. Since the survey was conducted at the end of the Delta wave, just as the Omicron wave was beginning, we included a binary variable that measured whether one had been diagnosed with COVID-19 by a health professional (13% at the time of the survey).\u003c/p\u003e\n \u003cp\u003eTo control for the possibility that conspiracy beliefs might affect the relationship between information sources and vaccination, we included a conspiracy scale measured by summing seven items (range: 7\u0026ndash;28). Respondents were prompted with, \u0026ldquo;There are many views about the coronavirus episode. To what extent do you agree or disagree with the following?\u0026rdquo; They were presented with statements like \u0026ldquo;China created the coronavirus in a laboratory for its political gain,\u0026rdquo; and \u0026ldquo;The government is using coronavirus restrictions to see how much control they can exert over people\u0026rdquo;. The complete list of conspiracy scale items may be found in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eOur primary independent variables measured usage and perceptions of reliability for seven different information sources about COVID-19. We view information sources as divided into two broad categories, depending on the degree to which organizational or professional guidelines are generally viewed as constraining message content. More regulated sources include local health experts, national health experts (like the CDC), local government, and national government. Less regulated sources include social media such as Facebook and Twitter, as well as religious or community leaders, and journalists (broadcast, print, or online). Their perceived reliability was measured as an ordinal variable with four response categories: (4) very reliable, (3) somewhat reliable, (2) rarely reliable, (1) never reliable.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalytic method\u003c/h2\u003e\n \u003cp\u003eTwo regression analyses are used to assess the outcome variables owing to variation in their measurement format. We use logistic regression to examine predictors significantly associated with vaccination status. In the first model, we assess independent variables that are related to vaccination controlling for covariates. In the second, we include only those predictors that were statistically significant in the first model to establish changes in the magnitude or direction of the relationship net of controls. Next, we employ linear regression to assess the nature of the relationship between the independent variables and anti-vaccination sentiment. As mentioned above, the first model assesses the associations between the main predictor variables and anti-vaccination sentiment, controlling for covariates. The second model assesses the changes when only the statistically significant independent variables are used in the analysis. Both analyses highlight significant dimensions associated with the outcome variables, showing that information about COVID-19 from local health experts, community/religious leaders, and social media was significantly associated with both vaccination and anti-vaccination sentiments.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable 3 is available in the Supplementary Files section.\u003c/p\n\u003cp\u003e[Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e about here.]\u003c/p\u003e\n\u003cp\u003eBivariate analyses on all the variables in the study were completed to assess the correlations between variables. There were significant correlations between the main independent variables and both outcome variables. The only exception was social media, which did not have a significant relationship with vaccination status. Additionally, while the relationship between vaccination status and the primary independent variables were all positive, social media had a negative effect. On the other hand, the correlation effects between anti-vaccination sentiments and the independent variables were negative with two exceptions. Information from religious leaders and social media (perceived as reliable) had a positive relationship with anti-vaccination sentiments.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRegression Analysis of Vaccination and Anti-Vaccination Sentiments\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVaccinated\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAnti-vax Sentiments\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLived in community setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfrican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.40\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.39\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.34\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.32\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNative American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.57\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.52\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighest educational level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.20\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.22\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttend religious service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNow employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolitical Spectrum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.21\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.16\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual household Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.23\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosed with COVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConspiracy Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eReliability of Information\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMore Regulated Sources\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocal Health experts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.17\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNational health experts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocal Government\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNational Government\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLess Regulated Sources\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity \u0026amp; Religious leaders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial Media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.13\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.16\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJournalists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eModel 1 Count R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.808\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eModel 2 Count R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.803\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eModel 3 Adjusted R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.565\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eModel 4 Adjusted R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;0.559\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e[Table 4 about here.]\u003c/p\u003e\n\u003cp\u003eRegression analyses for both outcomes are shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The impact of all independent dimensions on vaccination status and anti-vaccination sentiment is shown in Models 1 and 3, while the reduced Models 2 and 4 contain only significant predictors. Tests for multicollinearity in the predictors and the anti-vaccination sentiments model were conducted by examining Variance Inflation Factors and Tolerance Values. The results show that all predictors had a VIF less than 4 and tolerance values greater than 0.2, indicating no issues with multicollinearity.\u003c/p\u003e\n\u003cp\u003eSeveral demographic and control variables are significant and positively related to both vaccination status and anti-vaccination sentiments. Gender and Native American or Hispanic status were not significant in any model we tested. Model 2 shows that older, employed individuals with higher educational attainment and household income were more likely to be vaccinated, as were Asians and those who lived in communal settings. Having been diagnosed with COVID-19 was also associated with an increased likelihood of vaccination. African Americans, those who lean politically to the right, those who have children, and those with higher conspiracy scores were less likely to be vaccinated.\u003c/p\u003e\n\u003cp\u003eAnti-vaccination sentiments were higher for those who live in communal settings and attend religious service, as shown in Model 4. Those who leaned right politically, had been diagnosed with COVID-19, and had higher scores on the conspiracy scale were also more likely to hold anti-vaccination sentiments, whereas African Americans (compared with Whites) and older individuals were less likely to hold such opinions.\u003c/p\u003e\n\u003cp\u003eLog odds coefficients for the perceived reliability of information sources are presented in the second part of Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Positive effects on vaccination (Model 2) were significant for (a) local health experts, (b) national health experts (like CDC), and (c) local government. Those who perceive social media and religious/community leaders as more reliable were less likely to be vaccinated. The predicted probability that a respondent received a vaccine if they trusted information from regulated sources increases by approximately 10% from lowest (never) to highest (very) values for all significant sources except national government, which increases by 20%.\u003c/p\u003e\n\u003cp\u003eAnti-vaccination sentiments were relatively consistent with vaccination itself with respect to their association with information sources. Coefficients for the reliability of information sources were negative for local health experts but positive for social media, community/religious leaders, and journalists. In other words, those who viewed the information from \u003cem\u003elocal\u003c/em\u003e health experts as reliable were less likely to hold anti-vaccination views, while people who trusted information from social media, religious/community leaders, and other media were more likely to hold anti-vaccination sentiments, holding all other variables constant.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eInformation plays a critical role in public health management. The success of the COVID-19 vaccination campaign cannot be uncoupled from the flow of information and public perceptions of source reliability. Before COVID-19, studies indicated that the crux of the vaccination issue may have been trust or distrust in governmental and health institutions [25]. During COVID-19, a 2021 study indicated that increased trust in health experts was an important predictor of receiving a vaccine [26], while a 2022 study suggested that information from regulated sources was associated with individual health choices [27].\u003c/p\u003e\n\u003cp\u003eThe national survey analyzed here sought to examine specific information sources and their association with both vaccination and anti-vaccination beliefs. Information sources vary in the extent to which organizational constraints limit or encourage certain kinds of outputs, as well as the extent to which people view them as reliable. Such sources include both those that tend to be more regulated (e.g., health experts and government sources) and those that are less regulated (social media, religious and community leaders, and journalists). The results here show that information from regulated sources was positively associated with vaccination status while information from less regulated sources was negatively associated. Correspondingly, anti-vaccination sentiments were higher for those who viewed social media and community/religious leaders as reliable. Those who viewed local health experts as reliable were less likely to hold such sentiments. As Harper and colleagues suggest, this may be partially due to the more consistent communication of the threat posed by the virus by health and government experts [28]. The association between information from local health experts and anti-vaccination sentiments is important because it raises the issue of trust. Perhaps a plausible basis for this nexus is the relationship that exists between local experts and their communities.\u003c/p\u003e\n\u003cp\u003eRespondents who viewed social media, community/religious leaders, and journalists as reliable may have received diverse or even contradictory information, resulting in a lower probability of vaccination and greater anti-vaccination sentiments. Sieber and colleagues suggested that the impact of social media was to reduce the likelihood that people get the COVID-19 vaccine [24]. Indeed, there may be a cascading effect. Once people begin to consume anti-vaccination materials they may seek and find more such content [29]. In a network analysis of 100 million users on Facebook, 4.2% held anti-vaccination views, 6.9% were in favor of vaccination and 74.1% were \u0026ldquo;undecided\u0026rdquo; about the topic [30]. Journalism may have a similar effect. Thus, people with distinct political leanings seek out journalists who share their political inclinations exposing people to a narrower range of information through their social media platforms. We note that information from national and local governments was not significantly associated with anti-vaccination sentiments. However, it must be noted that some government officials used social media platforms to communicate the need for COVID-19 measures. It is difficult to measure the impact these officials had on vaccination or vaccine hesitance because different platforms appeal to specific demographics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile several dimensions were related to vaccination but not anti-vaccination beliefs (education, income, children, and the reliability of local/national government), one interesting finding that emerges from this analysis are sources of inconsistency: factors related to behavior (vaccination) and belief (anti-vaccination) in unexpected ways. At the bivariate level, vaccinated respondents are less likely than those who are unvaccinated to hold any of the five beliefs in our anti-vaccination scale. Our conventional understanding is that behavior and attitudes tend toward consistency\u003csup\u003e[3]\u003c/sup\u003e [31]. Whether the causal direction is (a) from sentiment to behavior or (b) from behavior to sentiment, we typically expect vaccination to be negatively correlated with anti-vaccination sentiments.\u003c/p\u003e\n\u003cp\u003eConsistency of the correlates of behavior and belief is implied if those factors \u003cem\u003epositively\u003c/em\u003e associated with vaccination are \u003cem\u003enegatively\u003c/em\u003e associated with anti-vaccination sentiment. In statistical terms, evidence for consistent predictors of behavior and belief would be coefficients in Models 2 and 4 that are reversed in sign: (a) factors positively associated with vaccination should be negatively associated with anti-vaccination sentiments, while (b) factors negatively associated with vaccination should be positively associated with anti-vaccination sentiments.\u003c/p\u003e\n\u003cp\u003eYet, as Table 3 shows, only one positive factor (age) is negatively associated with anti-vaccination sentiment, while only two negative factors (right-leaning politics and conspiracy beliefs) are positively associated with anti-vaccination beliefs. Inconsistency in predictors is common, beginning with race. African Americans were less likely to have had a vaccine, but also less likely to hold anti-vaccine sentiments. This finding supports prior work that found African Americans less willing to vaccinate than other races [3-4]. The literature on vaccine behavior and distrust of the government also shows the reluctance of African Americans to receive vaccines [32-33]. Further, some studies highlight the challenge some minority groups experience accessing vaccines in the nascent stage of the program [34]. However, they are \u003cem\u003eless\u003c/em\u003e likely than Whites to hold anti-vaccination sentiments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCommunity living and having a positive COVID-19 status also had inconsistent relations with both outcome variables. Those who lived in community settings and had been diagnosed with COVID-19 were more likely than others to have been vaccinated but were also more likely to hold anti-vaccination sentiments. One possible explanation for this is that each of these factors was associated with social network pressures to vaccinate, while at the same time placing individuals in contact with others who had diverse beliefs.\u0026nbsp;Many group living arrangements imposed vaccination mandates as a requirement to remain in these statuses. Hence, people who lived in group settings were more likely to have received a vaccine. Yet at the same time, they may have been in contact with individuals unhappy about such requirements and exposed to anti-vaccination sentiments. Lastly, being diagnosed with COVID-19 was also positively associated with receiving at least one vaccine and holding anti-vaccination sentiments. While the present data does not tell us whether vaccination occurred before or after COVID-19 diagnosis, one possible reason for this association was the belief that having COVID-19 itself is sufficient to acquire antibodies and mandated vaccination increased receptivity to antivaccination beliefs. Future studies should explore the reasons for these inconsistencies.\u003c/p\u003e\n\u003cp\u003eThis analysis has three limitations. First, the study was cross-sectional, such that causal implications remain speculative. Second, the large sample size can produce statistically significant relationships that are not always substantively significant. Finally, data were gathered at the end of 2021 and the findings cannot be generalized to the second half of the pandemic as it evolved over time.\u003c/p\u003e\n\u003cp\u003eThis study attempted to understand the relationships between both sociodemographic factors and COVID-19 information sources with vaccination behavior and anti-vaccination sentiments. Since regulated sources\u0026mdash;government and health experts\u0026mdash;are associated with increased vaccination and lower anti-vaccination sentiments, agents from these sources need to think carefully about their relationships with social media, community leaders, and journalists if they wish to amplify and mitigate casualties. Local government should also improve the funding of local public health communication in future pandemics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Tommy G. Thompson Center on Public Leadership.\u0026nbsp;The authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data will be publicly available immediately after the principal findings regarding vaccines, conspiracies, and Covid impacts have been disseminated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN.A. and W.S. wrote the main manuscript text and P.M. collected the data. N.A. and X.Y. analyzed the data. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang Y. and Liu Y. 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Public Health 2021;(9), 698111\u003cem\u003e \u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eMasiero, M. Mazzoni, D. Pizzoli, S.F.M.; Gargenti, S. Grasso, R.; Mazzocco, K. Pravettoni, G. The Individuals\u0026rsquo; Willingness to Get the Vaccine for COVID-19 during the Third Wave: A Study on Trust in Mainstream Information Sources, Attitudes and Framing Effect. Behavioral Science 2022; 12, 399. https://doi.org/10.3390/bs12100399\u003c/li\u003e\n\u003cli\u003eHarper, C.A., Satchell, L.P., Fido, D. et al. Functional Fear Predicts Public Health Compliance in the COVID-19 Pandemic. International Journal Mental Health Addiction 2021;19, 1875\u0026ndash;1888\u003cem\u003e. \u003c/em\u003ehttps://doi.org/10.1007/s11469-020-00281-5\u003c/li\u003e\n\u003cli\u003eAbul-Fottouh, Deena \u0026amp; Song, Melodie \u0026amp; Gruzd, Anatoliy. (2020). Examining algorithmic biases in YouTube\u0026rsquo;s recommendations of vaccine videos. International Journal of Medical Informatics\u003cem\u003e \u003c/em\u003e2020; 140. 104175. 10.1016/j.ijmedinf.2020.104175. \u003c/li\u003e\n\u003cli\u003eJohnson, N.F., Vel\u0026aacute;squez, N., Restrepo, N.J. et al. The online competition between pro- and anti-vaccination views. Nature 2020;582, 230\u0026ndash;233. https://doi.org/10.1038/s41586-020-2281-1\u003c/li\u003e\n\u003cli\u003eSiegel, J., T., Navarro, M., A., Tan, C., N., and Hyde, M., K. Attitude-behavior consistency, the principle of compatibility, and organ donation: A classic innovation. Health Psychol 2014; 33(9): 1084-91.\u003c/li\u003e\n\u003cli\u003eFreimuth, Vicki S., Amelia M. Jamison, Ji An, Gregory R. Hancock, and Sandra Crouse Quinn. Determinants of Trust in the Flu Vaccine for African Americans and Whites. Social Science \u0026amp; Medicine 2017; 193:70\u0026ndash;79.\u003c/li\u003e\n\u003cli\u003eJamison, A. M., Quinn S. C., and Freimuth V. S. You Don\u0026rsquo;t Trust a Government Vaccine: Narratives of Institutional Trust and Influenza Vaccination among African American and White Adults. \u003cem\u003eSocial Science \u0026amp; Medicine\u003c/em\u003e 2019; 221:87\u0026ndash; 94.\u003c/li\u003e\n\u003cli\u003eAbba-Aji M., Stuckler, D., Galea, S., Mckee, M. Ethic/racial minorities and migrants \u0026lsquo;access to COVID-19 Vaccines: A Systematic Review of Barriers and Facilitators. Journal of Migration and Health 2022; Vol. 5 100086.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e The number of eligible respondents within the Alchemer survey pool at the time of our survey was 25,098. These are U.S. citizens who have volunteered to take surveys in exchange for a small fee, similar to the Qualtrics survey platform. The first individuals who were eligible and completed the survey were our respondents. Others would not have had the opportunity, though they might have responded to the invitation, because they were too late. Quotas for gender, age, and so forth were established to represent the U.S. population, as described.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e \u003cspan\u003e Community living was left to the respondent\u0026rsquo;s interpretation. Generally, this is a type of residential facility like a nursing home or assisted living setting, but respondents may also have considered other arrangements as community living.\u003c/span\u003e \u003c/li\u003e\u003cli\u003e\u003cspan\u003e This assumption does not always apply in the public health literature.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"vaccines, anti-vaccination, information sources, COVID-19","lastPublishedDoi":"10.21203/rs.3.rs-4288648/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4288648/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAmong the issues that remained contentious throughout the pandemic was vaccination: its efficacy, side effects, and the general reluctance of a substantial segment of the population to get vaccinated. The aim of this paper is to understand the role of health information sources in anti-vaccination sentiment and the decision to vaccinate. Linear and logistic regression models were used to analyze data from an online survey of adults in the United States in late 2021 (n=10,221). The results of the study showed that: (a) information from local and national health experts had a significant positive association with getting the COVID-19 vaccine and a negative relationship with holding anti-vaccination sentiments while (b) information from social media and community/religious leaders had the opposite effect. Overall, this study highlights the importance of public health systems in the dissemination of information on vaccinations during pandemics.\u003c/p\u003e","manuscriptTitle":"Information Sources and Vaccination in the COVID-19 Pandemic","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-10 13:47:45","doi":"10.21203/rs.3.rs-4288648/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-02T11:48:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-16T13:45:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99239586984298368899855928106359508286","date":"2024-07-09T17:33:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-18T21:08:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147437198430308137904395598305060337358","date":"2024-05-10T18:36:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-10T17:18:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-06T11:25:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-06T11:18:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2024-04-18T15:05:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae69257f-0fa0-405a-ae97-2d34e14daa7f","owner":[],"postedDate":"May 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-21T15:59:20+00:00","versionOfRecord":{"articleIdentity":"rs-4288648","link":"https://doi.org/10.1186/s12982-024-00266-y","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2024-10-16 15:57:01","publishedOnDateReadable":"October 16th, 2024"},"versionCreatedAt":"2024-05-10 13:47:45","video":"","vorDoi":"10.1186/s12982-024-00266-y","vorDoiUrl":"https://doi.org/10.1186/s12982-024-00266-y","workflowStages":[]},"version":"v1","identity":"rs-4288648","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4288648","identity":"rs-4288648","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}