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. 2021 Apr 16;16(4):e0250123. doi: 10.1371/journal.pone.0250123

COVID-19 and vaccine hesitancy: A longitudinal study

Ariel Fridman 1,*, Rachel Gershon 1, Ayelet Gneezy 1
Editor: Valerio Capraro2
PMCID: PMC8051771  PMID: 33861765

Abstract

How do attitudes toward vaccination change over the course of a public health crisis? We report results from a longitudinal survey of United States residents during six months (March 16 –August 16, 2020) of the COVID-19 pandemic. Contrary to past research suggesting that the increased salience of a disease threat should improve attitudes toward vaccines, we observed a decrease in intentions of getting a COVID-19 vaccine when one becomes available. We further found a decline in general vaccine attitudes and intentions of getting the influenza vaccine. Analyses of heterogeneity indicated that this decline is driven by participants who identify as Republicans, who showed a negative trend in vaccine attitudes and intentions, whereas Democrats remained largely stable. Consistent with research on risk perception and behavior, those with less favorable attitudes toward a COVID-19 vaccination also perceived the virus to be less threatening. We provide suggestive evidence that differential exposure to media channels and social networks could explain the observed asymmetric polarization between self-identified Democrats and Republicans.

Introduction

Vaccinations are among the most important public health tools for reducing the spread and harm caused by dangerous diseases [1]. The World Health Organization estimates that vaccines prevented at least 10 million deaths between 2010–2015 worldwide [2]. Despite considerable evidence showing vaccines are safe [3, 4], there is increasing skepticism toward vaccination [5, 6]. Vaccine hesitancy has led to a decline in vaccine uptake and to an increase in the prevalence of vaccine-preventable diseases (VPDs) [7, 8]. Ironically, the objection to vaccines is commonly a consequence of their effectiveness—because individuals have lower exposure to VPDs, they are less concerned about contracting them [9], which consequently leads to greater vaccine hesitancy [10]. The COVID-19 pandemic has created a new reality where individuals are faced with a previously unknown disease and its effects, providing a unique opportunity to investigate vaccine attitudes during a period of heightened disease salience. The present research reports findings from a longitudinal study conducted during the COVID-19 health crisis, in which we measured changes in attitudes toward a prospective vaccine, as well as shifts in vaccine attitudes in general.

Factors influencing vaccine attitudes and behaviors

Past research has identified a variety of situational and individual-level factors that influence vaccine attitudes and behavior, the most prominent of which are risk perceptions and demographic characteristics.

Assessments of risk are influenced by both cognitive evaluations (i.e., objective features of the situation such as probabilities of outcomes) and affective reactions [11], as well as by contextual factors (e.g., the information that is most available or salient at the time [12]). For example, research shows that media coverage plays a significant role in determining the extent to which we take threats seriously [13]. When individuals perceive heightened risk of a threat, they become more favorable toward interventions that mitigate that threat, including vaccination (for a meta-analysis on the effect of perceived risk on intentions and behaviors, see [14]). In the case of COVID-19, this would suggest more positive attitudes toward a vaccine and greater likelihood to get vaccinated. Indeed, research suggests that individuals should exhibit a greater interest in vaccinations during a pandemic because disease threat is more salient [15].

Past efforts to improve vaccine attitudes have had limited success or even backfired; for example, messages refuting claims about the link between vaccines and autism, as well as messages featuring images of children who were sick with VPDs, had negative effects on vaccine attitudes among those who were already hesitant to vaccinate [16]. In contrast, messaging that increases disease threat salience has shown promise in reducing vaccine hesitancy [5], and there is evidence suggesting that increased threat salience for a particular disease may also increase intentions to vaccinate for other diseases [17]. Building on these findings, we expected to find an increase in pro-vaccine attitudes and in individuals’ interest in a COVID-19 vaccine when the perceived threat of the COVID-19 virus increased.

Vaccine attitudes are also influenced by a variety of demographic and ideological factors (for a review, see [18]). For example, perceptions of vaccine risk differ among individuals of different ethnic backgrounds [19], and there is extant work demonstrating a positive correlation between socioeconomic status (SES) and vaccine hesitancy [20, 21]. Socio-demographic factors are also linked to vaccine-related behaviors: among college students, those whose parents have attained a higher level of education are more likely to get immunized [22], and researchers have identified age as a predictor for receiving the influenza vaccine [23].

Political ideology is another well-documented determinant of vaccine-related attitudes and behaviors. Despite a common belief that liberals tend toward anti-vaccination attitudes in the United States, there is strong evidence that this trend is more present among conservatives [24, 25]. According to a recent Gallup Poll, Republicans are twice as likely to believe the widely debunked myth that vaccines cause autism [26]. Recent work has shown that exposure to anti-vaccination tweets by President Trump—the first known U.S. president to publicly express anti-vaccination attitudes—has led to increased concern about vaccines among his supporters [27]. Based on these findings, and in conjunction with the sentiments expressed by the White House that diminished the significance of the pandemic [28], we expected to find diverging trends between Democrats and Republicans.

The current research

We collected vaccine-related attitudes of individuals living in the U.S. over a six-month period. Beginning in March 2020, we elicited attitudes from a cohort of the same individuals every month. We began data collection before any COVID-19 lockdown measures were in place (i.e., prior to the nation’s first shelter-in-place order [29]). Hence, our data spans the early phase of the pandemic, when there were fewer than 2,000 total confirmed cases in the U.S., through the following six months, at which point cumulative cases reached over 5.3 million [30].

Despite our prediction—that a public health crisis would increase disease threat, consequently increasing pro-vaccine attitudes and interest in vaccination—our data show an overall decrease in favorable attitudes toward vaccines. A closer look at the data revealed that political orientation explains more variance than any other socio-demographic variable. Specifically, participants who identify as Republican showed a decrease in their intention to get the COVID-19 vaccine and the influenza vaccine as well as a general decrease in pro-vaccine attitudes, whereas Democrats’ responses to these measures did not show a significant change during this period.

Our work is the first, to our knowledge, to longitudinally measure individuals’ attitudes toward vaccines. In doing so, our findings advance the understanding of how vaccine attitudes might change during an unprecedented public health crisis, revealing a strong association between political party affiliation and vaccine attitudes.

Methods

Participants

We recruited a panel of U.S. residents on Amazon’s Mechanical Turk platform to respond to multiple survey waves. To incentivize completion of all waves, we informed participants their payment would increase for subsequent surveys. Participants were paid 30 cents for wave 1, 40 cents for wave 2, and 60 cents for waves 3 and 4, $1.00 for wave 5, and $1.20 for wave 6. In addition, participants were informed that those who completed the first three waves would enter a $100 raffle. The median survey completion time was 5.5 minutes. The sample size for the first wave was 1,018, and the number of participants ranged from 608–762 on subsequent waves (see S1 Table for attrition details). This project was certified as exempt from IRB review by the University of California, San Diego Human Research Protections Program (Project #191273XX).

Our panel represents the broad and diverse population of the United States. The first wave sample included participants from all 50 states (except Wyoming) and Washington D.C., with an age range of 18 to 82 years old (mean = 38.48, median = 35). Approximately half (53%) identified as male, 46% as female, and.6% as other. The racial makeup in our sample was: 80% White, 9% Asian, 6% Black or African American, 4% multiple racial or ethnic identities, and 1% other. Relative to the U.S. Census (2019) [31] estimates, our sample over-represents White and Asian individuals, and under-represents Black or African American individuals and other racial groups.

We elicited political affiliation using a 6-point Likert scale, ranging from Strongly Republican to Strongly Democratic. In wave 1, 62% identified as Democrats and 38% identified as Republican, which is consistent with results from the most recent General Social Survey (GSS) [32]. There was no significant change in mean political identity from wave 1 to waves 2–6 (see S2 Table). We classified participants as Democrats or Republicans using wave 1 political party affiliation. See S2 Appendix for additional details about the correlation of political party affiliation with age, gender, and SES.

Questions and measures

Our primary measure of interest was participants’ stated intention to get the COVID-19 vaccine when it becomes available. We were also interested in their perceptions of COVID-19 threat, general vaccination attitudes, and intention to get the flu shot. For all measures, except flu shot intentions, we combined multiple items to create a composite measure (see S2 Table for specific questions and construct compositions). Questions designed to measure general vaccination attitudes were adapted from prior work [33].

Additional measures of interest were participants’ trust in broad institutions (media, local government, and federal government). These trust measures followed different trends from each other, and therefore were not combined. At the end of the survey, participants responded to demographic questions. We retained all questions used in wave 1 throughout all six waves (our survey included additional items not reported in this paper; see S2 and S3 Tables for a complete list of measured items).

Data and analysis plan

Only participants with non-missing and non-duplicated responses were included in the analyses (see S1 Appendix for additional details). For all outcomes of interest, we tested for linear trends over time using a fixed effects regression specification [34]. All regression results include individual-level fixed effects, and standard errors are clustered at the individual level, to adjust for within-person correlation. We used this approach to control for the impact of omitted or unobserved time-invariant variables. P-values are not adjusted for multiple testing (see [35]). All analyses were conducted using R (version 4.0.2), and regressions were run using the package “fixest” (version 0.6.0). All materials, data, and additional analyses including robustness checks can be found here: https://osf.io/kgvdy/.

Results

We report results for three different vaccination-related measures: attitudes toward a COVID-19 vaccine, general vaccination attitudes, and flu shot intentions. All measures showed a decreasing trend (Ps < .001, except flu shot intentions where p = .05) for the 6-month duration of the study, indicating a reduction in pro-vaccination attitudes and intention to get vaccinated (COVID-19 and influenza vaccines). See S4 Table for full results of all regressions.

Heterogeneity in trend by political party

To better understand whether the decline in vaccine attitudes over time was driven by a particular factor, we used a data-driven approach, regressing all demographic characteristics on vaccine attitudes, in separate regressions. These demographics included education, income, SES, race, gender, an item measuring whether participants considered themselves to be a minority, whether the participant has children, and political party. Education, income, and SES were median split; race and gender were dummy coded; and political party affiliation was dichotomized into Democrat or Republican. Among all demographic characteristics, separating time trends by political affiliation (by adding an interaction term) attained the greatest adjusted within-R2 in explaining vaccination attitude measures. In other words, political party affiliation explains the greatest within-individual variation in vaccine attitudes over time.

An analysis of responses by political affiliation revealed that the observed decreasing trend in all three vaccine measures was mostly driven by participants who identified as Republican (all Ps < .05), whereas Democrats’ responses showed either no significant trend (for COVID-19 vaccination and flu shot intentions: Ps >.67) or a significantly less negative time trend (general vaccination: p < .001). For these regressions, and moving forward, all results included interactions between wave and political party as well as interactions for wave and age, and wave and SES, to control for potentially different time trends associated with these variables. In each regression we also tested whether the strength of political affiliation moderates the observed results, and we reported the result when it did. We also conducted ANOVAs to compare mean responses for the outcomes of interest between Democrats and Republicans, separately for each wave (see S5 Table).

COVID-19 vaccination attitudes (Fig 1, Panel A)

Fig 1. Vaccination attitudes and intentions by political affiliation (March–August 2020).

Fig 1

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Points represent means, and error bars represent 95% confidence intervals. All scale responses range from 1 to 7.

A two-item construct (r = .78) was created, with greater values corresponding to more favorable responses.

In wave 1, Democrats (M = 5.39, SD = 1.55) had more favorable attitudes toward a COVID-19 vaccine than Republicans (M = 4.57, SD = 1.76; t = -7.38, p < .001, d = -.48, 95% CI = [-.61, -.35]). Among Democrats, there was no significant time trend (β = .02, SE = .04, p >.67) whereas Republicans’ responses followed a decreasing time trend (β = -.09, SE = .05, p = .046). These trends were significantly different from each other (β = -.11, SE = .02, p < .001).

General vaccination attitudes (Fig 1, Panel B)

A ten-item construct (α = .95) was created, with greater values corresponding to a more positive attitude toward vaccination in general.

In wave 1, Democrats (M = 5.83, SD = 1.15) expressed more favorable general vaccination attitudes than Republicans (M = 5.17, SD = 1.31; t = -7.91, p < .001, d = -.52, 95% CI = [-.66, -.39]). Although both Democrats and Republicans had a decreasing time trend (Democrats: β = -.04, SE = .02, p = .029; Republicans: β = -.09, SE = .02, p < .001), the trend for Republicans was significantly more negative (β = -.04, SE = .01, p < .001).

Flu shot intentions (Fig 1, Panel C)

We asked participants whether they plan to get the flu shot next year, with greater values indicating greater intentions.

In wave 1, Democrats (M = 4.84, SD = 2.34) indicated greater intentions to vaccinate against the flu than Republicans (M = 4.35, SD = 2.39; t = -3.15, p = .002, d = -.21, 95% CI = [-.34, -.08]). Among Democrats, there was no significant time trend (β = .01, SE = .04, p = .86), suggesting their vaccination intentions remained largely stable. Republicans’ responses, however, revealed a decreasing time trend (β = -.12, SE = .04, p = .005), and the two trends were significantly different from each other (β = -.12, SE = .02, p < .001).

Our analyses revealed an interaction with political affiliation strength among Republicans, whereby participants who identified as more strongly Republican had a more negative time trend (β = -.05, SE = .02, p = .027). This interaction was not significant for Democrats (β = -.02, SE = .01, p = .19).

Perceived threat of COVID-19 (Fig 2)

Fig 2. Perceived threat of COVID-19 by political affiliation (March–August 2020).

Fig 2

Open in a new tab

Points represent means, and error bars represent 95% confidence intervals. All scale responses range from 1 to 7.

A three-item construct (α = .82) was created, with greater perceived threat about COVID-19.

In wave 1, Democrats (M = 4.26, SD = 1.25) expressed greater perceived threat of COVID-19 than Republicans (M = 3.90, SD = 1.39; t = -4.14, p < .001, d = -.40, 95% CI = [-.27, -.14]). Democrats’ responses showed an increasing time trend (β = .08, SE = .04, p = .033), indicating they became increasingly concerned about the threat posed by the virus over time. Among Republicans, there was no significant time trend (β = -.01, SE = .04, p = .83). These trends were significantly different from each other (β = -.09, SE = .02, p < .001). While our data does not render causal claims, it is possible that the divergence in COVID-19 threat perceptions over time among Republicans and Democrats contributes to the divergence in vaccine attitudes between these groups over time. We revisit this proposition in the General Discussion.

Our analyses revealed an interaction with political affiliation strength among Democrats—participants who identified as more strongly Democrat had a more positive time trend (β = .03, SE = .01, p = .019), suggesting an increasing threat perception over time. This interaction was not significant for Republicans (β = .01, SE = .02, p = .61).

Trust in broad institutions

The measures of trust in media, local government, and federal government were not highly correlated (α = .66), and were therefore analyzed separately.

Trust in media (Fig 3, Panel A). In wave 1, Democrats (M = 3.61, SD = 1.66) reported greater trust in the media than Republicans (M = 2.73, SD = 1.65; t = -8.12, p < .001, d = -.53, 95% CI = [-.66, -.39]). There was no significant time trend for either Democrats (β = .02, SE = .04, p = .57) or Republicans (β = -.05, SE = .04, p = .20). However, the trend for Republicans was significantly more negative (β = -.07, SE = .02, p < .001). The different trends we observe for Democrats and Republicans with respect to trust in the media may explain the divergence in perceived threat and vaccine attitudes between these groups over time (see General discussion).

Fig 3. Trust in broad institutions by political affiliation (March–August 2020).

Fig 3

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Points represent means, and error bars represent 95% confidence intervals. All scale responses range from 1 to 7.

Trust in local government (Fig 3, Panel B). In wave 1, Democrats (M = 4.07, SD = 1.60) indicated lower trust in local government than Republicans (M = 4.28, SD = 1.60; t = 2.01, p = .045, d = .13, 95% CI = [.003,.26]). Among Democrats, there was no significant time trend (β = -.06, SE = .04, p = .18), though among Republicans, there was a decreasing time trend (β = -.11, SE = .05, p = .015). These trends were significantly different from each other (β = -.06, SE = .02, p = .004).

Trust in federal government (Fig 3, Panel C). In wave 1, Democrats (M = 2.96, SD = 1.67) expressed lower trust in the federal government than Republicans (M = 4.08, SD = 1.60; t = 10.52, p < .001, d = .68, 95% CI = [.55,.82]). Both Democrats and Republicans had decreasing time trends (Democrats: β = -.08, SE = .04, p = .036; Republicans: β = -.10, SE = .04, p = .025). These trends were not significantly different from each other (β = -.02, SE = .02, p = .37).

Attrition

To rule out differential attrition, we tested whether the composition of our sample (i.e., age, gender, and political party) changed over time (see S1 Table). Specifically, we tested whether participants who responded to waves 2–6 were significantly different at baseline (wave 1) from the full sample at baseline. The only significant change detected (Ps < .05) was with respect to participants’ age, though the differences were small—the average age was 38.5 at baseline, and remained between 39.9 and 40.8 at baseline among participants who responded to subsequent waves. We found no other systematic pattern of attrition among our participants.

General discussion

Over the course of six months of the COVID-19 pandemic, beginning with a relatively early phase prior to any U.S. directives to stay home (March 2020) and continuing through a cumulation of over 5 million cases (August 2020), we found a decrease in pro-vaccine attitudes and COVID-19 vaccination intentions, as well as reduced intentions to get the influenza vaccine. These findings are contrary to our prediction that increased salience of COVID-19 would improve attitudes toward vaccines.

Our analyses identify political ideology as the best predictor of the decreasing time trend across our three vaccine-related attitudes and intentions measures. In particular, we found that while Democrats’ responses remained fairly stable over time, Republicans shifted away from their lower initial responses and from Democrats’ responses, leading to increased polarization throughout the six-month period.

Contrary to the polarization observed in our data, social and behavioral scientists have long argued that groups facing threats often come together, demonstrating stronger social cohesion [36], and more cooperative behaviors [37, 38]. Researchers have also found that individuals’ sense of shared identity plays a role in promoting cooperative behavior in response to threat [39–41]. Considering our results in the context of these findings might suggest that our respondents’ sense of shared identity was dominated by their political ideology, as opposed to a broader (e.g. American) identity.

What might be going on?

Although the nature of our data does not render causal claims, it highlights potential explanations. First, we note that participants’ ratings of perceived COVID-19 threat followed a similar diverging pattern by party affiliation to our three vaccine-related measures during the study period. Democrats perceived COVID-19 threat to be greater at the start of the study than Republicans did, and this gap widened significantly as the study progressed. This trend is consistent with previous research showing that vaccine hesitancy is related to perceived risk of a threat; when a VPD threat level is low, individuals are less motivated to take preventative action (i.e., immunize; for a review, see [42]).

Our data offers one potential explanation for the polarization of threat perception: Republican and Democratic participants in our study reported consuming different sources of information. The most commonly checked news source for Republicans was Fox News (Republicans: 50%, Democrats: 8%; χ2 = 164.55, p < .001) and for Democrats was CNN (Democrats: 47%, Republicans: 23%, χ2 = 43.08, p < .001, see S6 Table). Corroborating this proposition, a Pew Research Center poll conducted in March 2020 found that 56% of respondents whose main news source is Fox News believed that “the news media have greatly exaggerated the risks about the Coronavirus outbreak,” whereas this was only true for 25% of those whose main news source is CNN [43]. Of note, Facebook and Instagram, were also in the top four most consumed news sources for participants affiliated with either party. Extant work describes these platforms as echo chambers [44, 45], which may exacerbate partisan exposure to news and information.

Another trend highlighted by our data shows that similar to vaccine attitudes, Republicans’ trust in the media decreased significantly more during our study than Democrats’, suggesting these patterns might be related. According to Dr. Heidi Larson, an expert on vaccine hesitancy and founder of the Vaccine Confidence Project, misinformation regarding vaccinations is more likely to take root when individuals do not trust the information source [46]. Future research might further examine the role of trust in the media on vaccine attitudes.

While trust in media or media exposure may be driving COVID-19 threat perceptions and vaccine attitudes, there are many other possible explanatory factors that are not captured by our data or analyses. For example, it is possible that threat perceptions were influenced by how a respondents’ county or state was affected by COVID-19; up until June 2020, COVID-19 cases were more common in Democrat-leaning states [47], which might have amplified its salience early on and influenced attitudes and behavior. Further, although we included individual-level fixed-effects which control for all time invariant participant characteristics, and controlled for different trends by age and SES, we cannot rule out the possibility that other factors (e.g., educational attainment or population density) may have influenced the observed trends. Finally, as our data collection began after the onset of COVID-19, it is possible that the trend we observe for Republicans represents a return to a pre-pandemic baseline of vaccine-related attitudes.

Contributions

This work advances our understanding of how health-related attitudes evolve over time. Our focus on vaccine-related attitudes and intentions is important because experts agree that having enough people vaccinate against COVID-19 is key to stemming the pandemic [48]. More broadly, negative attitudes toward vaccination in general, and reduced vaccine uptake, is increasingly a public health concern [49]. This research provides insight into the trends of such vaccine hesitancy, underlining the importance of risk salience and its roots in ideology and media exposure.

This work also contributes to our understanding of political parties and polarization. Numerous anecdotes and reports have demonstrated a partisan divide in Americans’ response to the COVID-19 pandemic. For example, research found greater negative affective responses to wearing a face covering among politically right (vs. left) leaning individuals [50]. Here, we show that although these observations are valid, the reality is more nuanced. For example, our analyses reveal that polarization on vaccine measures—both attitudes and intentions—is driven primarily by self-identified Republicans’ gradual movement away from their initial responses whereas Democrats’ responses remained largely stable. This insight has important practical implications: It informs us about the dynamics of individuals’ attitudes, bringing us closer to understanding the underlying factors that influence attitudes and behaviors. Equipped with this knowledge, one could design more effective communications and interventions.

Note on methodology and data availability

The present study contributes to a small but growing literature in the social sciences using longitudinal data [51]. Using a longitudinal methodology allowed us to track individual-level changes over time. Merely observing a single point in time would allow us to observe across-group differences, but would lack the bigger picture of how polarization between these groups evolved. Another key advantage of panel data is that it allows us to include individual-level fixed effects, which control for the impact of omitted or unobserved time-invariant variables. Finally, panel data allows for more accurate inference of model parameters [52].

While the focus of this paper is vaccine attitudes, our broad dataset offers a unique opportunity to understand attitudes and behavior over time. Due to the richness of our data, its unique nature, and its timeliness, we believe it is important to make it available to other researchers interested in exploring it and publishing additional findings. The complete dataset is available at https://osf.io/kgvdy/ (see S2 and S3 Tables for all items collected).

Supporting information

S1 Appendix. Additional information about sample exclusions.

(DOCX)

Click here for additional data file. (22.9KB, docx)
S2 Appendix. Additional information about political party affiliation.

(DOCX)

Click here for additional data file. (23.4KB, docx)
S1 Table. Attrition table.

(DOCX)

Click here for additional data file. (30.1KB, docx)
S2 Table. Summary table of measures and constructs included in the text.

(DOCX)

Click here for additional data file. (31.8KB, docx)
S3 Table. Summary table of measures excluded from the text.

(DOCX)

Click here for additional data file. (41.5KB, docx)
S4 Table. Regression results.

(DOCX)

Click here for additional data file. (29.9KB, docx)
S5 Table. Outcome measures by political party affiliation.

(DOCX)

Click here for additional data file. (28.3KB, docx)
S6 Table. Summary of news sources.

(DOCX)

Click here for additional data file. (25.4KB, docx)

Data Availability

All data and code are publicly available on the Open Science Framework at https://osf.io/kgvdy/.

Funding Statement

UC San Diego Global Health Initiative (GHI): awarded to all authors; Project number: 1001288. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. https://medschool.ucsd.edu/som/medicine/divisions/idgph/research/Global-Health/grant-recipients/2019-2020/Pages/Faculty-Postdoc-Travel-and-Research.aspx.

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Decision Letter 0

Valerio Capraro

8 Jan 2021

PONE-D-20-35660

COVID-19 and Vaccine Hesitancy: A Longitudinal Study

PLOS ONE

Dear Dr. Fridman,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please find below the reviewer's comments, as well as those of mine.

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We look forward to receiving your revised manuscript.

Kind regards,

Valerio Capraro

Academic Editor

PLOS ONE

Additional Editor Comments:

I have now collected one review from one expert in the field. I was unable to find a second reviewer. However, the one review I could collect is very detailed; moreover, I am myself familiar with the topic of this manuscript. Therefore, I feel confident in making a decision with only one review. The review is positive but suggests a major revision. I agree with the reviewer. Therefore, I would like to invite you to revise your work following the reviewer's comments. I only have one more comment, beyond those of the reviewer. You also look at the correlation between risk perception and vaccine hesitancy. I have recently published a paper where we look at the correlation between risk perception and intentions to wear a face mask. The results are in line with your study. I think it could be interesting to relate these works.

Capraro, V., & Barcelo, H. (2020). The effect of messaging and gender on intentions to wear a face covering to slow down COVID-19 transmission. Journal of Behavioral Economics for Policy.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The manuscript describes a very interesting longitudinal study conducted on a sample of US citizens, regarding their attitudes towards vaccines in general and intention to get COVID-19 vaccine.

However, while the research itself, and its results, are very interesting and with potentially useful implications, I feel that the quality of the report needs to be improved, as I will outline in detail below.

Firstly and foremost, I find a bit awkward the the choice of the authors of introducing parts that should pertain to the discussion of the results in the introduction (i.e. lines 62-65 and 92-100).

The same goes for the results section: in this section, the authors included not only the results, but also a (a bit confused, in my opinion) explanation of data analyses. I recommend the authors to re-organize this section, adding a paragraph in "methods" to explain their analyses plan before describing results: an example (but not exhaustive) are lines from 148 to 153, as these are methods and not results. Another example are lines 169-172. Re-organizing these sections will greatly increase readability and clarity.

Regarding data analyses, I have some concerns: I was expecting an approach based on the analysis of variance (ANOVA), to address means differences within waves and between different groups. So, I'm not really sure that the approach adopted by the authors is the most suitable. However, I expect that the aforementioned re-organization of the methods and results sections will help (me and the future readers) to understand the authors' choices, and the authors to justify the methods they adopted.

Moreover, given sample size and the number of tested hypotheses, I would like the authors to address the fact that some of their "significances" were rather marginal (e.g. see p-value=.046 considered significant at line 188). I feel like the authors should address this by either adopting a more conservative value of p (instead of the usual p=.05), or by adding some note of caution in the discussion for those results that are only marginally significant. On the same page, I would like the authors to add effect sizes were applicable, e.g. Cohen's d (or similar) when reporting t-tests.

Finally, one last concern regarding the sample: were any strategies used to check data quality? Unfortunately, using data from panel results sometimes in some participants being "professional respondents": were any countermeasures taken (e.g. screening of multivariate outliers, uncommont response patterns or survey completion times)? if not, this should be address and discussed by the authors in the manuscript.

The bottom line is: the study is of great importance on a paramount topic. The results themselves are interesting, with potentially useful implications. It also has been conducted rigorously, although I would like some methodological choices to be explained better. However, the quality of the manuscript needs to be improved, in particular for what concerns the organization of the sections.

**********

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Reviewer #1: Yes: Lorenzo Palamenghi

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PLoS One. 2021 Apr 16;16(4):e0250123. doi: 10.1371/journal.pone.0250123.r002

Author response to Decision Letter 0

3 Feb 2021

February 3, 2021

COVID-19 and Vaccine Hesitancy: A Longitudinal Study

Dear Dr. Capraro,

Thank you for the opportunity to revise our manuscript, titled “COVID-19 and Vaccine Hesitancy: A Longitudinal Study” for resubmission to PLOS ONE. We greatly appreciated the constructive feedback we received. Below we have outlined our response to each point that you and the reviewer raised in the review.

AE: I have recently published a paper where we look at the correlation between risk perception and intentions to wear a face mask. The results are in line with your study. I think it could be interesting to relate these works.

Thank you for bringing this paper to our attention. We incorporated it into the general discussion, on lines 329-331.

R: I find a bit awkward the choice of the authors of introducing parts that should pertain to the discussion of the results in the introduction (i.e. lines 62-65 and 92-100).

Thank you for your feedback on the layout of the introduction. We streamlined the introduction, as per your suggestion, and removed lines 62-65. We revised lines 92-100 as well, which constitute the last few lines of the introduction and transition to the empirical section. We believe that these lines provide an important preview of the results which may be helpful to readers seeking a quick summary. However, if you still feel that this way of introducing the results is inappropriate, we will change it.

R: The same goes for the results section: in this section, the authors included not only the results, but also a (a bit confused, in my opinion) explanation of data analyses. I recommend the authors to re-organize this section, adding a paragraph in "methods" to explain their analyses plan before describing results: an example (but not exhaustive) are lines from 148 to 153, as these are methods and not results. Another example are lines 169-172. Re-organizing these sections will greatly increase readability and clarity.

Based on your feedback, we have made changes throughout the methods, results, and discussions sections to increase clarity. For example, as the reviewer suggested we moved lines 148-153 and 169-172 to the methods section (now 140-145 and 119-125, respectively). We also included a paragraph describing our analysis plan. We believe that these edits have improved the manuscript. Thank you for the suggestion.

R: I was expecting an approach based on the analysis of variance (ANOVA), to address means differences within waves and between different groups. So, I'm not really sure that the approach adopted by the authors is the most suitable. However, I expect that the aforementioned re-organization of the methods and results sections will help (me and the future readers) to understand the authors' choices, and the authors to justify the methods they adopted.

We re-organized the methods and results section, and hope they are now clearer. In the newly added analysis plan paragraph (lines 140-148), we provided additional justification for the fixed-effects regression model we used to analyze our data. Furthermore, based on your suggestion, we added an ANOVA table with all results to the manuscript (lines 174-176, table S4).

R: Moreover, given sample size and the number of tested hypotheses, I would like the authors to address the fact that some of their "significances" were rather marginal (e.g. see p-value=.046 considered significant at line 188). I feel like the authors should address this by either adopting a more conservative value of p (instead of the usual p=.05), or by adding some note of caution in the discussion for those results that are only marginally significant. On the same page, I would like the authors to add effect sizes were applicable, e.g. Cohen's d (or similar) when reporting t-tests.

The point about P-values is well taken. We included a sentence (line 145) explicitly pointing out that the P-values reported are not adjusted for multiple testing. Part of the reason we decided not to adjust the P-values is due to the fact that there is not a broad consensus on the correct way to do this. However, all data is available to readers who would like to adjust the P-values in their preferred way. Furthermore, the particular P-value the reviewer mentioned, regarding the time trend for Republicans, is not pertinent to our claim of divergence between Democrats and Republicans over time (which are P < .001 on all the vaccination attitudes and intentions measures). We also added Cohen’s d effect sizes for all t-tests, as suggested, as well as 95% confidence intervals for the effect sizes.

R: Finally, one last concern regarding the sample: were any strategies used to check data quality? Unfortunately, using data from panel results sometimes in some participants being "professional respondents": were any countermeasures taken (e.g. screening of multivariate outliers, uncommon response patterns or survey completion times)? If not, this should be address and discussed by the authors in the manuscript.

While data from Amazon’s Mechanical Turk has advantages and disadvantages, with extant research examining the reliability and quality of this sample (e.g., Goodman, Cryder, and Cheema 2012), we have confidence in our choice to use this population for our study. We can further use our own data to demonstrate the quality of our sample. One indication that participants are paying attention is that the demographic makeup of our sample is stable over time, indicating that they did not respond to these questions at random. Due to your concerns, we ran an additional robustness check in which we removed participants with completion times below the 10th percentile, corresponding to less than 3 minutes. We found a similar pattern of results, though some coefficients were no longer significant at the .05 level, which is unsurprising given the smaller sample size. These include: overall decline in flu shot intentions (p = .06), Republican’ decline in COVID-19 vaccination attitudes (p = .10), Democrats’ decline in general vaccination attitudes (p = .08), Democrats’ increase in perceived threat of COVID-19 (p = .06). Importantly, the difference in trends between Democrats and Republicans remained significant in all cases. This robustness check and a robustness check that includes only participants who completed all 6 waves of the study can be easily run using our code. We would also argue that if the participant quality were low or if participants were not paying attention, this would add noise to the data, and work against finding significant results, rather than introduce a systematic bias.

Reference: Goodman, Joseph K., Cynthia E. Cryder, and Amar Cheema. "Data collection in a flat world: The strengths and weaknesses of Mechanical Turk samples." Journal of Behavioral Decision Making 26, no. 3 (2013): 213-224.

Once again, we very much appreciate the in-depth feedback we received and think that the paper is much improved as a result of these changes. We hope that you will agree and we look forward to receiving your reply.

Sincerely,

Ariel Fridman

PhD Candidate in Marketing, Rady School of Management, UC San Diego

Rachel Gershon

Assistant Professor of Marketing, Rady School of Management, UC San Diego

Ayelet Gneezy

Professor of Behavioral Sciences and Marketing, Rady School of Management, UC San Diego

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (16.1KB, docx)

Decision Letter 1

Valerio Capraro

31 Mar 2021

COVID-19 and vaccine hesitancy: A longitudinal study

PONE-D-20-35660R1

Dear Dr. Fridman,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Valerio Capraro

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In the first round I raised two different sets of comments: some on the methodology and others on the clarity of the report.

As for the methodological concerns I raised, I feel like the authors' resposes are adequate, and I have no further concerns to this regards.

As for the clarity, I think that the reorganization of the various sections has much improved the quality of the manuscript and the overall clarity. Still, the authors have decided to leave a small "anticipation" of the results (which, to me, really looks like a discussion of the results) in the introduction.

At this point, I think it comes to a matter of personal preferences: personally, I believe that the abstract should give the readers a quick summary, and that the introduction should introduce (and not anticipate or summarize) the presented study. However, those few lines do not impact the overall clarity, so I won't ask for further revisions and I'll let the authors (or the editor, eventually) decide whether these lines should be changed or not, as this in my opinion goes beyond the scope of the peer review: the study is solid, interesting, quite well reported, and should be accepted for publication.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Lorenzo Palamenghi

Acceptance letter

Valerio Capraro

7 Apr 2021

PONE-D-20-35660R1

COVID-19 and vaccine hesitancy: A longitudinal study

Dear Dr. Fridman:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Valerio Capraro

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Appendix. Additional information about sample exclusions.

    (DOCX)

    Click here for additional data file. (22.9KB, docx)
    S2 Appendix. Additional information about political party affiliation.

    (DOCX)

    Click here for additional data file. (23.4KB, docx)
    S1 Table. Attrition table.

    (DOCX)

    Click here for additional data file. (30.1KB, docx)
    S2 Table. Summary table of measures and constructs included in the text.

    (DOCX)

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    S3 Table. Summary table of measures excluded from the text.

    (DOCX)

    Click here for additional data file. (41.5KB, docx)
    S4 Table. Regression results.

    (DOCX)

    Click here for additional data file. (29.9KB, docx)
    S5 Table. Outcome measures by political party affiliation.

    (DOCX)

    Click here for additional data file. (28.3KB, docx)
    S6 Table. Summary of news sources.

    (DOCX)

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    Attachment

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