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Article

The Role of Gubernatorial Affiliation, Risk Perception, and Trust in COVID-19 Vaccine Hesitancy in the United States

by
Ammina Kothari
1,*,
Stephanie A. Godleski
2 and
Gerit Pfuhl
3,4
1
The Gwen Ifill School of Media, Humanities, and Social Sciences, Simmons University, Boston, MA 02115, USA
2
Department of Psychology, College of Liberal Arts, Rochester Institute of Technology, Rochester, NY 14623, USA
3
UiT The Arctic University of Norway, 9037 Tromsø, Norway
4
Department of Psychology, Norwegian University of Science and Technology, 7491 Trondheim, Norway
*
Author to whom correspondence should be addressed.
COVID 2025, 5(8), 118; https://doi.org/10.3390/covid5080118
Submission received: 13 June 2025 / Revised: 16 July 2025 / Accepted: 23 July 2025 / Published: 28 July 2025
(This article belongs to the Section COVID Public Health and Epidemiology)
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Abstract

Background/Objectives: Vaccine hesitancy is becoming an increasing concern, leading to preventable outbreaks of infectious diseases. During the COVID-19 pandemic, the United States served as an intriguing case study for exploring how risk perception and trust in health authorities, including scientists, are influenced by government policies and how these factors affect vaccine hesitancy. Methods: We conducted a secondary analysis using the MIT COVID-19 Survey dataset to investigate whether risk perception and trust differ between states governed by Democratic or Republican governors. Results: Our analysis (n = 6119) found that participants did not vary significantly by state political affiliation in terms of their sociodemographic factors (such as age, gender, self-rated health, education, and whether they live in a city, town, or rural area), their perceived risk for the community, or their ability to control whether they become infected. However, there was a difference in the perceived risk of infection, which was higher in states governed by Republicans. Trust also varied by gubernatorial affiliation, with higher levels of trust reported among residents of Democratic-leaning states. We also found a strong mediation effect of trust on vaccine hesitancy, but this was not the case for risk perception. Conclusion: Therefore, it appears that vaccine acceptance relies on trust in health authorities, which is influenced by governmental policies. State officials should work with local health officials to build trust and increase timely responses to public health crises.

1. Introduction

Despite the significant loss with regard to life and economic welfare in the United States due to COVID-19 [1], receptiveness to the vaccine has been variable [1]. Vaccine hesitancy is not a new phenomenon; indeed, vaccine hesitancy was listed as a top threat to global health in 2019 [2] and has been documented in the United States since vaccines have been available [2]. For example, measles cases continue to be reported in the United States, likely due to reductions in vaccinations [2], and this year, we have seen a significant spike in measles cases. According to the CDC [3], as of 29 May 2025, there were a total of 1088 confirmed measles cases in the United States.
A systematic review by Yasmin et al. [4] demonstrated that COVID-19 vaccine acceptance varied by state and region within the United States. Additionally, researchers [4,5] found that individuals who identified as male and White showed higher rates of vaccine acceptance, whereas those identifying as female and Black had lower rates of acceptance. Over time, vaccine acceptance rates in the United States have been reported to increase [4]; however, these rates may differ based on sociodemographic and political factors, as well as the stage of vaccination (e.g., initial vaccinations, boosters; [6]).
Trust is the foundation of a healthy community. Trust is built when the public has confidence that authorities will make sound decisions and put the community’s best interests first, especially when facing a health crisis. While trust is influenced by a variety of factors (e.g., [7]), trust in government has significantly influenced vaccine decision-making both historically (e.g., [8,9]) and specifically regarding the COVID-19 vaccine in several countries [10]. Distrust in vaccine manufacturing and the government has been a central factor in vaccine hesitancy over the years in the United States. Concerns about the risks associated with vaccination [11] and socio-political motivations also contribute to this hesitancy [12]. Similar to previous vaccines for infectious diseases [11], mistrust has been identified as a key factor contributing to vaccine hesitancy in relation to COVID-19 [13]. Research shows that trust in science and scientists is generally lower among those who are affiliated with Republicans compared to Democrats, and this gap has widened in recent years, potentially increasing the political influence on a critical aspect of collective decision-making [14].
Misinformation during the pandemic [15,16] resulted in increased distrust in vaccines. Before the announcement of vaccine availability in the United States, Daly and Robinson [17] observed a significant rise in vaccine hesitancy, which decreased after the announcement [18,19,20]. Therefore, providing accurate and factual communication [21,22] and fostering trust in these communication channels [13] is essential. In terms of vaccine guidance, Americans rated healthcare officials and scientists as credible sources [23], and their trust in these experts was consistently linked to lower vaccine hesitancy throughout the pandemic [16,24]. Those who expressed greater concern about how the pandemic was handled in the United States also reported higher levels of hesitancy toward the vaccine [25].
Evidence indicates that trust in the government, along with vaccine hesitancy, may be influenced by political affiliation in the United States [20,26]. Specifically, research has shown that a conservative affiliation is linked to lower trust in the government regarding vaccine safety, programs, and activities [26], as well as decreased vaccine acceptability [27]. Conversely, those with more moderate [27] and liberal [6] political leanings have been found to exhibit higher levels of vaccine acceptability. Declines in vaccine-related attitudes and intentions to be vaccinated for both COVID-19 and influenza have been particularly evident among individuals identifying as Republicans [18]. These trends are also observable at the level of state partisanship, with greater vaccine hesitancy reported in Republican-leaning states [20]. This may be further influenced by policies implemented by state leaders, as differences have been noted in COVID-19 policies [28,29] and patterns of COVID-19 testing, cases, and deaths [30] based on gubernatorial affiliation.
Political orientation is a strong predictor of vaccine hesitancy in the United States [31]. A vaccine monitoring study conducted in 2021 found that 86% of those affiliated with the Democrats and only 52% of those affiliated with the Republicans reported receiving at least one dose of the COVID-19 vaccine [32]. While researchers have identified several factors associated with COVID-19 vaccine hesitancy, little is known about how the political affiliation of governors affects public trust and support for vaccines. Baccini et al. [29] discovered that term limits and political ideology influence how state governors respond to health issues. In the context of COVID-19, Democratic governors were quicker to mandate practices that prioritize health and safety, while Republican governors tended to focus more on the economic impacts of health mandates. Research has also indicated that race, gender, and education play a diminished role in predicting vaccine hesitancy [19,20]. For instance, American men were just as likely as women to express hesitancy, and education and state partisanship did not alter the influence of gender. However, other studies [33] have shown that women report higher levels of vaccine hesitancy in the United States. In contrast, Wills and colleagues [34] found that COVID-19 vaccine hesitancy is greater among those identifying as Independents and Republicans compared to those identifying as Democrats.
Gubernatorial party affiliation may also influence how people perceive risk and their trust in health authorities, which may ultimately affect vaccine hesitancy [30]. An individual’s assessment of harm, defined as risk perception, is influenced by various factors, including risk awareness. This was summarized in a systematic review of COVID-19 risk perceptions [35]. When individuals have a low perception of risk or lack trust in health authorities and scientists, they are more likely to be hesitant about vaccines. For example, policies that enforce social distancing can create varying feelings of safety; some may feel safer, while others may feel less safe. However, it is less understood how gubernatorial party affiliation and policy mandates impact trust in local health authorities. Similarly, there is limited research on whether gubernatorial affiliation affects vaccine hesitancy after considering factors such as risk perception, trust, and demographics (including age, gender, education, and health). Statistically considering these factors is necessary, as the perceived risk and trust may vary across states and be influenced by gubernatorial policies. We propose that gubernatorial affiliation, and ultimately, policies during the COVID-19 pandemic, have an impact on risk perception and trust in information sources concerning COVID-19.
In this study, we explored the relationship between the political leanings of governors who set the public health response mandates and the emergence of vaccine hesitancy throughout the pandemic in the United States. Our research adds to the existing literature on COVID-19 vaccine hesitancy in the United States (e.g., [17,18,19,20,34]) by providing a state-level perspective on how trust in information sources and health risk perceptions influence, and function as a potential mechanism for, vaccine hesitancy.

2. Materials and Methods

We conducted a secondary analysis of the “COVID-19 Beliefs, Behaviors & Norms Survey” collected by the Massachusetts Institute of Technology (MIT)’s Sloan School of Management in collaboration with Facebook, with input from researchers at Johns Hopkins University, the World Health Organization, and the Global Outbreak Alert and Response Network (for the technical report, [36]). The survey protocol E-2294 was approved by the MIT Committee on the Use of Humans as Experimental Subjects. Data were collected over 19 bi-weekly cross-sections and included responses from the 51 states in the United States (50 + Puerto Rico). We used the Kaiser Family Foundation (KFF) [37] State Political Parties dataset to identify each governor’s political party affiliation and the National Academy for State Health Policy COVID-19 vaccine tracker (https://nashp.org/state-tracker/state-efforts-to-ban-or-enforce-covid-19-vaccine-mandates-and-passports/, accessed on 1 July 2025) to identify the state mandate on the COVID-19 vaccine based on the gubernatorial party affiliation.
Participants could quit the survey at any time and no response was forced. After some initial demographic items and the vaccine hesitancy item (see below), the order of other items (e.g. trust, risk perception) was randomized. There was a considerable attrition rate (less than 10% answered all items), as the entire survey took over 10 min. Since we are interested in how risk perception and trust affect vaccine hesitancy, we only included participants who answered all the demographic items and the vaccine hesitancy item and completed either the risk or the trust scale (see below).

2.1. Measures

2.1.1. State Party Affiliation

Among the 50 states for which we had the state party affiliation (not Puerto Rico), 28 had a Republican governor prior to the 2020 United States election. There was no change after the election.

2.1.2. Vaccine Hesitancy

Vaccine hesitancy was measured based on responses to a question that asked whether they “would choose to get vaccinated” and offered “yes,” “don’t know,” and “no” as answer options. Before the survey became available, the question contained the phrase “If a vaccine became available”. Once the vaccine was available, the question included the option to report whether the respondent had been vaccinated. We combined the responses from those who reported willingness to be vaccinated with those who reported being vaccinated, as vaccination was not mandatory for everyone in the United States.

2.1.3. Sociodemographic Information

The survey asked for respondents’ gender (male, female, other), age (in decades, under 20, 20–30, …, over 80), education (less than primary school, primary school, secondary school, college/university, graduate school), own health (excellent, very good, good, fair, poor), US state (one of the 50 states), and density (city, town, village or rural area). We coded their own health ordinally from poor = 0 to excellent = 4, and we coded education from less than primary school as 0 to graduate school as 4.

2.1.4. Trust

The survey assessed how much one trusts various sources of COVID-19 news and information sources. The sources were (a) local health workers, clinics, and community organizations, (b) scientists, (c) the World Health Organization (WHO), (d) government health authorities or other officials, (e) politicians, (f) journalists, (g) ordinary people one knows personally, and (h) ordinary people one does not know personally. We had no prediction for (e), (f), (g) and (h) with respect to gubernatorial affiliation, and therefore, they were not included in analyses.

2.1.5. Risk Perception

There were four items concerning risk perception, of which two addressed the community. (1) How dangerous do you think the COVID-19 risk is to your community? Rated on a 5-point Likert scale (Not at all dangerous, Slightly dangerous, Moderately dangerous, Very dangerous, Extremely dangerous). (2) How likely is it that someone of the same age as you in your community becomes sick from COVID-19? Rated on a 5-point Likert scale (Not at all likely, Slightly likely, Moderately likely, Very likely, Extremely likely). (3) Control of infection was assessed with the following: Do you agree with this statement? “I have control over whether I will get COVID-19,” rated on a 5-point Likert scale (Strongly disagree, Somewhat disagree, Neither agree or disagree, Somewhat agree, Strongly agree). (4) Own infection severity was measured with “How serious would it be if you became infected with COVID-19?” and this was rated on a 3-point Likert scale (Not at all serious, Somewhat serious, Very serious). We hypothesized a difference by gubernatorial affiliation for (2) and (3) but not for (1) and (4).

2.1.6. Analysis

The data was preprocessed and the causal mediation model run in R Studio (2024.4.1, [38]) with the package mediation [39].
The MIT dataset included 65,361 respondents from the United States, collected in two-week waves from July 2020 to April 2021. We excluded data from wave 9 (26 October 2020, to 9 November 2020) because it coincided with the announcement of vaccines and the US election. Additionally, we removed respondents living in Puerto Rico, resulting in a total of 62,202 participants. Since vaccine hesitancy was the primary focus of our analysis, we included only those respondents who answered questions related to this topic. We also excluded participants who did not provide information on their age, gender, education, self-rated health, and population density (rural, town, or city), which left us with a sample of 50,065 participants. Further refining our sample, we included only those who answered the questions regarding trust (in scientists, local health authorities, and the WHO) and the four items on risk perception. This reduced our sample size to 6119 participants. We also assessed whether there were any demographic differences between the larger sample of 50,065 and the reduced sample that included participants who answered the trust and risk perception items.
There was no significant difference in age between the largest sample with demographic data (n = 50,065) and the sample that included risk and trust information (n = 6119). The average ages were 43.79 and 44.06, respectively, resulting in a Cohen’s D of 0.00. Welch’s t-test showed t (55,612) = 630.12, p < 0.001. Similarly, there was no difference in education level, with average scores of 2.05 and 2.06, leading to a Cohen’s D of 0.04. The Welch’s t-test result was t (72,011) = 561.71, p < 0.001.
Furthermore, there was no difference in self-reported health, with average scores of 1.53 and 1.55, resulting in a Cohen’s D of 0.01 and a Welch’s t (66,827) = 354.08, p < 0.001. In terms of the gender distribution, the comparison showed no significant difference, with a Cramer’s V of 0.01, χ2(1) = 11.076, p = 0.004. Similarly, there was no notable difference in rural versus urban living status, with a Cramer’s V of 0.01, χ2(1) = 12.25, p = 0.001. Overall, the effect sizes were small, indicating no meaningful differences in demographic factors between the larger sample and the smaller subset used for the analyses. Therefore, we utilized the dataset with 6119 participants without performing any imputations.
We next conducted a causal mediation analysis using the risk and trust items as mediators. State gubernatorial political affiliation served as the predictor (IV), while vaccine acceptance was the outcome (DV). Given that the effect sizes were small, we created a risk score by summing the following items: risk to the community, risk of infection, risk of infection severity, and the reverse-coded control of infection. This risk score ranged from 2 to 16, with most participants scoring either 9, 10, 11, or 12. Similarly, we calculated an overall trust score by recoding the responses as follows: “Do not trust” was assigned a score of 0, “Somewhat trust” 1, and “Trust” 2. We summed the scores across the four trust items, resulting in a trust score that ranged from 0 to 8, with most participants scoring either 7 or 8. Note that the trust scores followed an increasing trend and were not normally distributed.

3. Results

3.1. Demographics

The final sample included 3925 women, 2175 men, and 19 individuals who identified as other in terms of gender. Among the participants, 45 were younger than 20 years old; 707 were between 20 and 29; 1176 were in the 30 to 39 age range; 1183 were aged 40 to 49; 1171 were 50 to 59; 1155 were 60 to 69; 581 were 70 to 79; and 101 were 80 years old or older. In terms of education, 22 participants had not completed primary school, 242 had completed primary school, 1052 had completed secondary school, 3348 had attended college or university, and 1455 had completed graduate school. When asked about their health, 141 participants rated it as poor, 932 as fair, 2221 as good, 2175 as very good, and 790 as excellent. Most respondents lived in cities (3133 individuals, or 51.2%), followed by those living in towns (1953 individuals, or 31.9%) and those residing in villages or rural areas (1033 individuals, or 16.9%).
We examined whether the demographic variables varied according to the state gubernatorial affiliation (Figure 1). We found no significant differences based on age, with the mean age being 44.49 years in states with a Democratic governor and 45.0 years in states with a Republican governor. The analysis yielded Welch’s t (6112.3) = 1.24, p = 0.215, Cohen’s d = 0.03. Similarly, there was no difference in the gender distribution: χ2(1) = 1.176, p = 0.556, and Cramer’s V = 0. Regarding the education levels, the results showed no statistically significant difference, with Welch’s t (6108.1) = 0.754, p = 0.451, Cohen’s d = 0.02 (mean education in Democratic states: 2.97, Republican states: 2.98, i.e., most went to college/university).
The self-reported health status also did not reveal any significant differences, as indicated by Welch’s t (6116.2) = 0.766, p = 0.444, Cohen’s d = 0.02 (Democratic states: 2.45, Republican states: 2.43). Lastly, there was no difference in population density, with χ2(1) = 3.197, p = 0.202, and Cramer’s V = 0.01. This suggests that any difference in risk perception and trust by gubernatorial party affiliation (see below) was not significantly due to differences in demographic variables.

3.2. Temporal Changes in Vaccine Hesitancy

Across the sample, we observed an increase in vaccine hesitancy (lower acceptance) in the summer of 2020, followed by a steady acceptance after the rollout of the vaccines (Figure 2). Additionally, there was greater hesitancy among participants living in Republican-led states: 53% responded with “No,” 49.8% said “Don’t know,” and 48.5% answered “Yes.” In contrast, participants in Democratic-led states showed different responses, with 47% indicating “No,” 50.2% stating “Don’t know,” and 51.5% answering “Yes.” This difference was statistically significant, with χ2 = 7.12, p = 0.029, and Cramer’s V = 0.03.

3.3. Risk Perception and Trust Based on Governor’s Political Affiliation

We expected variations in risk perception and trust based on the governor’s affiliation, but our analysis did not reveal a significant difference in perceived risk at the community level, Welch’s t (6098.1) = 0.295, p = 0.768, Cohen’s d = 0.007. However, there was a small difference in the perceived risk of infection, with individuals in Republican states reporting a higher risk (2.45) compared to those in Democratic states (2.35), Welch’s t (6111.9) = 3.595, p = 0.0003, Cohen’s d = 0.09. No significant difference was found concerning infection severity, Welch’s t (6109.9) = 1.083, p = 0.279, Cohen’s d = 0.03. Additionally, there was no difference regarding perceptions of controlling or becoming infected with COVID-19, Welch’s t (6115.4) = 1.528, p = 0.127, Cohen’s d = 0.04. Therefore, our hypotheses were only partially supported, and the effect sizes were small.
In terms of trust, we observed a small disparity in trust toward local health workers, with individuals in Republican-led states expressing less trust than those in Democratic-led states, χ2 = 11.8, p = 0.003, Cramer’s V = 0.04. Similarly, individuals in Democratic-led states demonstrated more trust in scientists, χ2 = 19.102, p < 0.001, Cramer’s V = 0.05, and in the WHO, χ2 = 27.434, p < 0.001, Cramer’s V = 0.06. Additionally, there was a notable difference based on the governor’s affiliation for trust in governmental health authorities, with greater trust observed in Democratic-led states, χ2 = 11.291, p = 0.004, Cramer’s V = 0.04. As shown in Table 1, trust was lowest for governmental health workers. Overall, residents in Democratic-governed states exhibited significantly more trust.

3.4. Gubernatorial Affiliation and Vaccine Hesitancy

Using risk as a mediator (Figure 3A), we found no significant average causal mediated effect (ACME estimate = −0.005, p = 0.074). However, the average direct effect (ADE estimate = 0.03, p < 0.001) and the total effect (ADE + ACME, estimate = 0.025, p < 0.001) were significant. The probability mediated (ratio of ACME to total effect) was −0.205, meaning there was a 20.5% suppression of risk in the relationship between the governor’s political affiliation and vaccine hesitancy. This suppression effect (indirect effect) was not significant (p = 0.074). This finding suggests that vaccine hesitancy was influenced by the governor’s affiliation, with an increase in hesitancy for those in states with Republican governors, but not by risk perception, which is not surprising given the similar risk perceptions observed.
In the causal mediation model with trust as a mediator (Figure 3B), we found a significant contribution of trust, with an ACME estimate of 0.022 (p < 0.001). Additionally, there was a significant average direct effect, with an ADE estimate of 0.003 (p = 0.006), and a significant total effect, estimated at 0.025 (p < 0.001). The probability estimate was 0.886, indicating that vaccine hesitancy was primarily driven by trust rather than the governor’s political affiliation.

4. Discussion

Overall, we found that the risk perception was similar across states with different gubernatorial affiliations. However, the trust levels were lower in states governed by Republican-affiliated governors, and this disparity contributed to variations in vaccine hesitancy. In particular, the results underscored the importance of legislative and health officials considering the role of trust during crisis situations in influencing individuals’ perceptions, and ultimately, their health behavior.
Vaccine hesitancy over time followed a similar pattern as in past research, with some variability based on the stage of vaccination [6], such that hesitancy demonstrated an increase during the summer of 2020 then subsequently steadily decreased, suggesting higher acceptance [17,18,19,20]. There was greater hesitancy by participants who were living in Republican-led states in comparison to Democratic-led states. Our analysis indicates that there was no significant variability in demographic factors typically associated with vaccine hesitancy (e.g., age, gender) across states with different gubernatorial affiliations (e.g., [4,5]), suggesting that this difference in hesitancy was likely not driven by potential demographic differences in the states and thus reinforcing the potential influence of political affiliation and related policies on vaccine hesitancy. This highlights the importance of considering the influence of political affiliation when developing policies and guiding their implementation.
In examining potential contributors and mechanisms concerning this difference by state-level affiliation in terms of hesitancy, the results suggested a higher perceived risk of infection in Republican-governed states, though the difference was relatively small in magnitude and there were no significant differences by state-level affiliation for other aspects of risk perceptions. Lower trust in local health workers as sources of news and information on COVID-19 was demonstrated in Republican-led states. Democratic-led states had higher expressed trust in scientists and the WHO, as well as in governmental health authorities. Therefore, prioritizing public health messaging by specific trusted sources, informed by political affiliation, in health crisis situations may lead to reduced hesitancy. However, governmental health authorities had the lowest expressed trust among the various sources of news and information regardless of affiliation. This finding supports work by [40], who also found that trust in the CDC declined significantly over time. The loss of trust in governmental health authorities could be explained by the evolving messages about the efficacy of masking and social distancing, which were then interpreted and enforced along partisan lines to varying degrees at the state level (see the National Academy for State Health Policy COVID-19 vaccine tracker). Importantly, the influence of vaccine hesitancy by gubernatorial affiliation appeared to be primarily driven by trust as a mediator, or mechanism, in the association between affiliation and hesitancy. Overall, these findings suggest the need for future research considering broader influences impacting trust in government health authorities to inform efforts to build trust and ultimately support policy and public health initiatives. Interestingly, although there was a small significant difference in state gubernatorial affiliation and perceived risk of infection, the perceptions of risk were not a significant mediator of this relation between affiliation and hesitancy. The results suggest the differential impact of state-level affiliation on trust in sources of COVID-19 news and information sources, which then subsequently impacted vaccine hesitancy. Efforts to reduce hesitancy may benefit from focusing on supporting trust in sources of health information, as opposed to increasing perceptions of the risk. Also, to be effective, these health messages must consider factors that influence risk perception and trust in authorities and information sources [41].
These findings have important public health implications. Indeed, state-level affiliation may serve as an indicator, regardless of demographic factors, of vaccine hesitancy and trust in sources of information may serve as an important mechanism in that association, suggesting that tailoring programs to support or enhance trust may have downstream effects on vaccine hesitancy and thus acceptance and uptake [42].

5. Limitations

While our study has important findings, it also has limitations. Our analysis relied on secondary data, and we did not collect information on participants’ political affiliations or race. Additionally, our sample consisted solely of active Facebook users who were willing to complete the survey, which may have underrepresented individuals who are not active on social media. Furthermore, governors often do not have full authority in their states [30], and the scope and strength of gubernatorial powers have variability from state to state. Future studies should investigate the influence of gubernatorial policies by examining party affiliation at both the state and local levels and the impact on the public’s trust in vaccines and health mandates, especially as skepticism about the efficacy of vaccines continues to rise. Future research could also examine the influence of variability in gubernatorial authority by state, as well as factors that may impact gubernatorial trust outside of affiliation (e.g., personality traits, term limits, etc. [29]).

Author Contributions

Conceptualization, A.K. and G.P.; methodology, A.K., S.A.G. and G.P.; software, G.P.; validation, A.K., S.A.G. and G.P.; formal analysis, G.P.; investigation, A.K. and G.P.; resources, G.P.; data curation, G.P.; writing—original draft preparation, A.K. and S.A.G.; writing—review and editing, A.K., S.A.G. and G.P.; visualization, G.P.; project administration, A.K. and G.P.; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The MIT Committee on the Use of Humans as Experimental Subjects approved the survey under protocol number E-2294 in 2020.

Informed Consent Statement

The researchers needed to sign an agreement to access anonymous data from Facebook.

Data Availability Statement

The data presented in this study are available on request from the corresponding author as the data were provided by a third-party and will require further permission before sharing.

Acknowledgments

We thank Facebook and MIT for sharing the data with the researchers. While we appreciate Facebook’s support for the social sciences, the platform continues to be a significant source of misinformation regarding vaccines.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
MITMassachusetts Institute of Technology
FBFacebook
CDCCenters for Disease Control and Prevention

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Covid 05 00118 g001
Figure 1. Descriptive statistics of the demographic variables by gubernatorial party. Legend: D, Democratic-led states; R, Republican-led states. (A), gender; (B), age; (C), education; (D), own health. There were no statistically significant differences for the demographic variables by gubernatorial party affiliation.
Figure 1. Descriptive statistics of the demographic variables by gubernatorial party. Legend: D, Democratic-led states; R, Republican-led states. (A), gender; (B), age; (C), education; (D), own health. There were no statistically significant differences for the demographic variables by gubernatorial party affiliation.
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Figure 2. Temporal fluctuation of vaccine acceptance (inverse of hesitancy) from July 2020 (wave 1) to March 2021 (wave 19). From wave 10, vaccine acceptance includes those already vaccinated and those stating they plan to be vaccinated. There was no data collection from 21st of September to 28th of September 2020.
Figure 2. Temporal fluctuation of vaccine acceptance (inverse of hesitancy) from July 2020 (wave 1) to March 2021 (wave 19). From wave 10, vaccine acceptance includes those already vaccinated and those stating they plan to be vaccinated. There was no data collection from 21st of September to 28th of September 2020.
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Figure 3. Path diagram for the causal mediation models. Coefficient for path a, b from the mediation and outcome model (intermediate steps in the causal mediation model). Coefficient for path c’ (ADE) from the causal mediation model. (A): Path diagram for risk mediating gubernatorial party affiliation on vaccine hesitancy, and (B): Path diagram for trust mediating gubernatorial party affiliation on vaccine hesitancy.
Figure 3. Path diagram for the causal mediation models. Coefficient for path a, b from the mediation and outcome model (intermediate steps in the causal mediation model). Coefficient for path c’ (ADE) from the causal mediation model. (A): Path diagram for risk mediating gubernatorial party affiliation on vaccine hesitancy, and (B): Path diagram for trust mediating gubernatorial party affiliation on vaccine hesitancy.
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Table 1. Trust by gubernatorial party affiliation.
Table 1. Trust by gubernatorial party affiliation.
Do Not TrustSomewhat TrustTrustProportion Trust
Governmental health workersD: 573
R: 656		D: 1529
R: 1479		D: 974
R: 872		D: 32%
R: 29%
Local health workersD: 135
R: 174		D: 993
R: 1048		D: 1959
R: 1810		D: 63%
R: 60%
ScientistsD: 139
R: 167		D: 651
R: 761		D: 2297
R: 2104		D: 74%
R: 69%
WHOD: 498
R: 623		D: 868
R: 897		D: 1721
R: 1512		D: 56%
R: 50%
Note: Last column shows that for all four information sources, there was more trust in Democratic-led states.
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Kothari, A.; Godleski, S.A.; Pfuhl, G. The Role of Gubernatorial Affiliation, Risk Perception, and Trust in COVID-19 Vaccine Hesitancy in the United States. COVID 2025, 5, 118. https://doi.org/10.3390/covid5080118

AMA Style

Kothari A, Godleski SA, Pfuhl G. The Role of Gubernatorial Affiliation, Risk Perception, and Trust in COVID-19 Vaccine Hesitancy in the United States. COVID. 2025; 5(8):118. https://doi.org/10.3390/covid5080118

Chicago/Turabian Style

Kothari, Ammina, Stephanie A. Godleski, and Gerit Pfuhl. 2025. "The Role of Gubernatorial Affiliation, Risk Perception, and Trust in COVID-19 Vaccine Hesitancy in the United States" COVID 5, no. 8: 118. https://doi.org/10.3390/covid5080118

APA Style

Kothari, A., Godleski, S. A., & Pfuhl, G. (2025). The Role of Gubernatorial Affiliation, Risk Perception, and Trust in COVID-19 Vaccine Hesitancy in the United States. COVID, 5(8), 118. https://doi.org/10.3390/covid5080118

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