Regional political climate’s moderating role in the association between political conservatism and COVID-19 vaccine hesitancy in the United States

Rachel E Dinero; William B Monti; Brittany L Kmush

doi:10.1371/journal.pone.0342063

. 2026 Feb 3;21(2):e0342063. doi: 10.1371/journal.pone.0342063

Regional political climate’s moderating role in the association between political conservatism and COVID-19 vaccine hesitancy in the United States

Rachel E Dinero ^1,², William B Monti ¹, Brittany L Kmush ^3,^*

Editor: Mickael Essouma⁴

PMCID: PMC12867218 PMID: 41632757

Abstract

There is an emerging body of evidence linking political conservatism and conservative political climate in the United States to COVID-19 vaccine hesitancy and uptake. The goal of the present research was to examine how political climate moderates the relationship between self-reported political conservatism and COVID-19 vaccine hesitancy and uptake. We collected online survey data from 683 participants between March 8 and April 19, 2023. Controlling for age, education, income, and race, there was an interaction between political conservatism and conservative political climate for both vaccine and booster hesitancy (β = .07, p = .03; β = .12, p < .001, respectively), such that liberals were less likely to be hesitant regardless of political climate. However, conservatives living in liberal political climates were less vaccine hesitant than their conservative counterparts living in conservative regions. A similar interaction was for the likelihood of receiving a COVID-19 booster (OR =.84, p = .049). Liberals were more likely to receive a booster regardless of political climate, while conservatives’ likelihood was associated with their political climate. Observed patterns linking liberal political climates with vaccine uptake among conservative individuals have important implications for vaccination efforts among conservative individuals in the United States.

Introduction

The COVID-19 pandemic has been unprecedented in recent history. From January 2020 until April 30, 2023, there were over 1.3 million deaths from COVID in the United States alone [1]. During that time, millions more Americans of all ages were hospitalized due to COVID with rates ranging from 179.8 per 100,00 during 2020 up to 521.2 per 100,000 during the 2021−2022 seasons [2]. Due to the high morbidity and mortality of this disease, many governments made vaccine development a priority. The vaccines greatly reduced the morbidity and mortality caused by COVID-19, with an estimated 2.5 million deaths averted and saved 15 million life-years worldwide by 2024 [3]. However, the rapid development and rollout of the vaccines was not without controversy. The relatively small sample sizes in the trails (over 20,000 people) could not rule out the possibility of missing rare adverse events, such as myocarditis in young, healthy males. Additionally, rigorous trials have not been completed to determine additional protection provided by subsequent or yearly vaccine doses in previously exposed populations [4].

The controversy around vaccines spread from the scientific and regulatory community to politics and the public, possibly exacerbated by the timing of the election cycle in the US [5]. In the US, political conservatism has been consistently associated with greater COVID-19 vaccine hesitancy [6–12] and lower COVID-19 vaccine uptake [10,13,14]. While political conservatism has been historically associated with lower trust in the scientific community, which is subsequently linked to vaccine hesitancy [15,16], the increased partisan divide in vaccine hesitancy may be attributed, at least in part, to the politicization of COVID-19 [17]. The influence of political orientation on vaccine hesitancy has been increasing since the onset of the pandemic and political orientation has become a better predictor of vaccine hesitancy than demographic variables, pandemic-related fear, and trust in health institutions [18].

The influence of political orientation on vaccine hesitancy and uptake may extend beyond individual orientation. There is an emerging body of evidence that the broader political climate within which an individual lives can influence COVID-19 vaccine hesitancy and uptake. For example, at both the county and the state level there was a positive association between Democratic vote share (i.e., % of people voting for the Democratic candidate) in the 2020 presidential election and COVID-19 vaccination rates from 2021−2022 [19]. Likewise, state-level 2020 Democratic presidential voting share was associated with higher state-level COVID-19 booster rates in 2022 [20]. Similarly, those who lived in states with higher Republican vote shares in the 2020 presidential election were more likely to report vaccine hesitancy in a 2021 sample of 443,680 participants [21]. While previous research has identified the unique impact of both political orientation and political climate on vaccination attitudes, there has been little research on the interaction between personal political orientation and local political climate.

The present research examines political climate as a potential moderator of the association between political orientation and COVID-19 vaccine hesitancy and uptake. Here, we examined the moderating role of political climate on the association between individual political orientation and COVID-19 vaccine hesitancy.

Materials and methods

Study design

This was an online self-reported study in which a nonprobability convenience sample was recruited from the United States between March 8 and April 19, 2023. The online survey assessed demographic control variables (i.e., age, gender, race, income, education, perceived vaccine access), predictor variables (i.e., political conservatism, regional political climate) and outcome variables (i.e., vaccine status, booster status, vaccine hesitancy, booster hesitancy) (S1 File).

Predictor variables

Demographic control variables (i.e., gender, race, income, and education) were assessed using single-item multi-option questions. Perceived vaccine access was assessed using a single-item question “If I needed to get a COVID-19 vaccine or booster, I could easily get it”, rated on a 5-point Likert scale from Disagree Strongly (1) to Agree Strongly (5). Political conservatism (i.e., a political ideology that emphasizes tradition, authority, and limited government intervention) was assessed across two items. The first item, assessing social conservatism (i.e., a political ideology that focuses on preserving traditional social institutions and opposing social change), asked participants to indicate their political orientation on “social issues (for example, abortion, gun rights, gay rights)”. The second item, assessing economic conservatism (i.e., a political ideology that emphasizes free markets and lower taxes), aske participants to indicate their political orientation on “economic issues (for example, taxation, government spending)”. Both items were rated on a 11-point sliding scale from strongly liberal (0) to strongly conservative (10). These two items were averaged to form a political conservatism scale.

While conservative ideology refers to individual beliefs, conservative regional climate refers to the broader ideological and partisan context in which an individual resides. We assessed regional political climate using zip code data provided by participants and 2020 presidential election results sourced from the New York Times [22]. Zip codes are postal codes used by the U.S. postal service to identify geographic delivery areas. These regions are often used as a proxy for local community characteristics [23]. Election results included the numbers of votes for the Democratic and Republican candidate (Biden and Trump, respectively) as well as the total number of votes for each Federal Information Processing Standard (FIPS) code. Political climate for each participant was determined by matching FIPS codes to corresponding zip codes. In cases where a zip code included multiple FIPS codes, data was averaged across these FIPS. Using the FIPS data for each zip code, we calculated the percent of Republican votes by dividing the number of Republican votes by the total number of votes (multiplied by 100). This reflects measurement processes in prior research [10,19,21]. We used this % Republican vote by zip code as a measure of conservative regional climate. Participants who did not provide zip code data were removed from the dataset.

Outcome variables

Self-reported vaccine status and booster status were assessed through individual items, “have you received a COVID-19 vaccine” and “have you received a COVID-19 booster”. The term “booster” is used in this analysis as this was the common term at the time to refer to subsequent doses of the COVID-19 vaccine after the initial vaccine series. Possible responses were yes, no, and I don’t know. Vaccine hesitancy (i.e., negative attitudes toward vaccination) was measured using the seven items from the Attitudes towards Adult Vaccination Scale [24], which assesses the perceived value of vaccines in general. We modified the items to assess attitudes specifically toward the COVID-19 vaccine [e.g., I fear the potential impact of the COVID-19 vaccine on my health in the future, I believe that the benefits of COVID-19 vaccination outweigh the potential risks (reverse-scored)]. Each item was rated on a 5-point Likert scale ranging from disagree strongly (1) to agree strongly (5). These items were averaged to form a vaccine hesitancy scale. Booster hesitancy was assessed using items from a 2022 survey on COVID-19 booster hesitancy [25].

Participant recruitment

Participants were a convenience sample recruited through Qualtrics Panels, an online research platform that manages participant recruitment and data quality for academic studies. Qualtrics Panels recruits participants through a network of verified online panel providers. Individuals voluntarily join these panels and provide demographic and behavioral information, which allows Qualtrics Panels to match participants to studies that fit specific eligibility criteria. Qualtrics Panels are widely used in academic research and have been shown empirically to produce samples with comparable demographic and behavioral characteristics to community and other panel sources when appropriate attention checks are used [26].

The data used in this study were part of a larger project assessing the impact of racial and socioeconomic marginalization on COVID-19 vaccine attitudes and behaviors [27]. The present research presents a novel analysis of the dataset from this project, focusing on political orientation and political climate, rather than marginalization. However, because of the initial goals in data collection, the sample was recruited across four quota groups based on race and income. Half of the sample was requested to be White (and no other racial identity), and half the sample was requested to be Black (and no other racial identity). Within each racial group, half of the participants were requested to be below the median annual income in the US [$70,000 [28]]. This resulted in four quota groups: White participants above-median-income, White participants below-median income, Black participants above-median income, and Black participants below-median income. Additionally, within each quota group it was requested that no more than 60% of the participants be of any gender.

No other exclusion criteria were specified; however, Qualtrics Panels does require participants to demonstrate consistent quality responding in order to remain in the panels system. Therefore, Qualtrics Panels screened out participants based on poor survey completion history. Qualtrics Panels screens data as it is collected and identifies participants that have data quality problems (e.g., no variance in responding, fast survey completion times that indicate participants are not reading items [more than two standard deviations below the mean completion time]. missing more than 25% of responses, passing required attention checks). To ensure data quality, we only include participants in our analysis that pass both Qualtrics Panels data quality screening and our own attention checks (S1 File), as recommended by previous research using Qualtrics Panels [26]. Qualtrics Panels conducted all data quality screens and marked participant’s data as either passing or not passing all quality screens.

Research ethics

Ethical approval was obtained prior to data collection from the first author’s institutional review board (protocol ER-F22-35, approved on 1 October 2022) and all research was conducted in accordance with the Declaration of Helsinki. All participants completed an informed, written consent as part of the online survey.

Statistical analyses

We performed confirmatory factor analysis on the items for the vaccine and booster hesitancy scales (S2 File) and the subsequent reliability of the scales was calculated using Cronbach’s alphas. Pearson correlations were used to assess correlations between all non-nominal variables (i.e., age, income, education, perceived vaccine access, vaccine and booster hesitancy, political conservatism, and conservative regional climate). Mean differences in categorical predictors (i.e., race and gender) across booster status and vaccine status were assessed using t-tests, and differences in frequencies of vaccine status and booster status by gender and race were assessed using X². Independent samples t-tests were used to assess differences in all non-nominal variables across vaccinated and unvaccinated participants. The same analyses were run to assess differences between boosted and unboosted participants. Multiple linear regression models were built to assess the association between political conservatism and conservative regional climate with vaccine hesitancy, controlling for age, gender, education, income, race, and perceived vaccine access. Ordinary least squares estimation was used with default standard errors. Model 1 included only control variables age, gender, education, income, race, and vaccine access as dependent variables. Model 2 included control variables and political conservatism and conservative regional climate. Model 3 included all variables from Model 2 and the interaction between political conservatism and conservative regional climate. This interaction assessed the extent to which the association between political conservatism and vaccine attitudes/behavior was moderated by conservative regional climate. The same process was used with booster hesitancy as the outcome variable to assess the association between political conservatism and conservative regional climate with booster hesitancy, controlling for age, gender, education, income, race, and perceived vaccine access. Additionally, we ran two sets of generalized linear models with logit link and model-based standard errors predicting vaccine status and booster status from the same three models described above. For all analyses, any participants with missing data on any of these variables were excluded from the analysis. All analysis and data visualization was conducted with R version 4.4.1 [29]. Two-sided p-values less than 0.05 were considered statistically significant.

Results

Qualtrics Panels collected data from 1777 participants, and identified 970 participants as not passing all quality screens (described above). We eliminated an additional nine participants whose open-ended responses did not make logical sense, resulting in a sample of 798 validated participants. We eliminated an additional 115 participants who did not provide valid zip code data (N = 100) or whose zip code did not have corresponding election data (N = 15). The final sample for these analyses included 683 participants. We conducted confirmatory factor analysis on the vaccine and booster hesitancy items (S2 File). The vaccine hesitancy items demonstrated adequate reliability (α = .93, 95% CI [.92,.93]) and were averaged to form the vaccine hesitancy scale. The booster hesitancy items demonstrated adequate reliability (α = .93, 95% CI [.93,.94]) and were averaged to form the booster hesitancy scale.

Participant characteristics

Participants ranged in age from 18 to 94 years (M = 46.29, SD = 18.94). Demographic characteristics of the sample are shown in Table 1. 519 (76%) of participants reported receiving the initial COVID-19 vaccine and 365 (53%) reported receiving at least one COVID-19 booster. Both vaccine hesitancy and booster hesitancy scores ranged from 1 to 5 out a possible 5 (M_vaccine = 2.39, SD_vaccine = 1.27; M_booster = 2.47, SD_booster = 1.32). Economic and social conservatism scores ranged from 0 to 10 out of a possible 10 (M_economic = 5.22, SD = 3.18; M_social = 5.00, SD = 3.22). The % Republican vote by zip code ranged from 6.53% to 88.63% (M = 45.08%, SD = 18.09%). Frequency distributions for vaccine hesitancy, booster hesitancy, political orientation, and % Republican vote are shown in Fig 1 and S3 Table.

Table 1. Participant age, gender, race, vaccine status, booster status, income, and education, United States, 2023 (N = 683).

Gender	N
Female	345 (50.5%)
Male	325 (47.6%)
Nonbinary/Transgender	11 (1.6%)
No Response	2 (0.3%)
Race
Black	342 (50%)
White	341 (50%)
Vaccine Status
Yes	519 (75.9%)
No	162 (23.7%)
No Response	3 (0.4%)
Booster Status
Yes	365 (53.4%)
No	308 (45.1%)
No Response	10 (1.5%)
Income
under $10,000	44 (6.4%)
$10,000 to $19,999	59 (8.6%)
$20,000 to $29,999	66 (9.7%)
$30,000 to $39,999	52 (7.6%)
$40,000 to $49,999	44 (6.4%)
$50,000 to $59,999	47 (6.9%)
$60,000 to $69,999	28 (4.1%)
$70,000 to $79,999	67 (9.8%)
$80,000 to $89,999	41 (6.0%)
$90,000 to $99,999	43 (6.3%)
$100,000 to $109,999	41 (6.0%)
$110,000 to $119,999	13 (1.9%)
$120,000 to $129,999	27 (4.0%)
$130,000 to $139,999	15 (2.2%)
$140,000 to $149,999	23 (3.4%)
$150,000 to $159,999	23 (3.4%)
$160,000 to $169,999	3 (0.4%)
$170,000 to $179,999	5 (.7%)
$180,000 to $189,999	1 (0.2%)
$190,000 to $199,999	16 (2.3%)
$200,000 or over	25 (3.7%)
Education
Below Primary School	1 (0.2%)
Primary School	6 (1%)
Secondary School	8 (1%)
High School Graduate	150 (22%)
Trade, Technical, or Vocational Training	39 (6%)
Some College	183 (27%)
Bachelor’s Degree	176 (26%)
Master’s Degree	89 (13%)
Professional Degree	10 (1%)
Doctoral Degree	21 (3%)

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Fig 1 — Four histograms depicting frequency distributions for vaccine hesitancy, booster hesitancy, political conservatism, and percent Republican vote by participant zip code.

Individual associations between predictors and outcomes

As shown in Table 2, there was no significant differences based on gender or race for vaccine hesitancy, booster hesitancy, vaccine status, or booster status. Vaccine hesitancy was positively correlated with booster hesitancy (r = .84, p < .001), economic conservatism (r = .27, p < .001), social conservatism (r = .31, p < .001), political conservatism (r = .30, p < .001), and conservative regional climate (r = .20, p < .001); and negatively correlated with age (r = −.16, p < .001), income (r = −.17, p < .001), and education (r = −.21, p < .001). Booster hesitancy positively correlated with economic conservatism (r = .22, p < .001), social conservatism (r = .25, p < .001), political conservatism (r = .25, p < .001), and conservative regional climate (r = .20, p < .001); and negatively correlated with age (r = −.15, p < .001), income (r = −.19, p < .001), education (r = −.19, p < .001). Unvaccinated participants were significantly younger (t = −3.54, p < .001) and reported lower income (t = −4.58, p < .001) as compared to vaccinated participants. Additionally, unvaccinated participants reported higher vaccine hesitancy (t = 18.18, p < .001) and booster hesitancy (t = 16.62, p < .001), and were higher in economic (t = 3.40, p < .001), social (t = 4.58, p < .001), and overall political conservatism (t = 4.21, p < .001) and lived in more conservative regional climates (t = 4.61, p < .001). Similarly, participants who reported not receiving a booster were significantly younger (t = −6.93, p < .001) and reported lower income (t = −6.17, p < .001) as compared to boosted participants. Unboosted participants also reported higher average vaccine hesitancy (t = 16.20, p < .001), booster hesitancy (t = 2.37, p < .001), economic conservatism (t = 2.80, p = .02), social conservatism (t = 18.82, p < .001), and overall political conservatism (t = 2.72, p < .001), and lived in more conservative regional climates (t = 3.98, p < .001).

Table 2. Vaccine hesitancy, booster hesitancy, vaccine status and booster status associations with sex, race, age, income, education, vaccine hesitancy, booster hesitancy, economic conservatism, social, conservatism, overall political conservatism, and political climate (% Republican vote by region), United States, 2023 (N = 683).

	Vaccine Hesitancy		Booster Hesitancy		Vaccine Status			Booster Status
	M	t	M	t	Vaccinated (N = 519) N	Not Vaccinated (N = 162) N	Χ ²	Boosted (N = 365) N	Not Boosted (N = 309) N	Χ ²
Sex
Female	2.45	−1.04	2.53	−1.37	263	81	.00	174	164	2.11
Male	2.34	−1.04	2.39	−1.37	248	77	.00	186	138	2.11
Race
Black	2.33	1.27	2.41	1.09	258	81	.00	172	162	1.68
White	2.45	1.27	2.52	1.09	261	81	.00	193	147	1.68
	r	p	r	p	M	M	t	M	M	t
Age	−.16	<.001	−.15	<.001	47.78	41.80	−3.54*	50.93	41.13	−6.93*
Income¹	−.17	<.001	−.19	<.001	8.56	6.40	−4.58*	9.22	6.74	−6.17*
Education²	−.21	<.001	−.19	<.001	6.32	5.46	−6.09*	6.48	5.70	−6.39*
Vaccine Hesitancy	–	–	.84	<.001	1.99	3.69	18.18*	1.78	3.13	16.20*
Booster Hesitancy	.84	<.001	–	–	2.05	3.78	16.62*	1.76	3.32	18.82*
Economic Conservatism	.27	<.001	.22	<.001	5.97	4.99	3.40*	5.54	4.96	2.37^a
Social Conservatism	.31	<.001	.25	<.001	6.01	4.67	4.58*	5.39	4.70	2.80*
Political Conservatism	.30	<.001	.25	<.001	4.84	5.99	4.21*	4.83	5.47	2.72*
Conservative Climate	.21	<.001	.20	<.001	43.32%	50.73%	4.61*	42.58%	48.09%	3.98*

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*p < .001

a p = .02

¹Income was assessed using $10,000 income brackets ranging from 1 (below $10,000) to 21 ($200,000 and over), 6 corresponds to $50,000 to $59,000 and 9 corresponds to $80,000 to $89,999 a year

²Education was an ordinal variable ranging from 1 (below primary school) to 10 (doctoral degree), 6 corresponds to “some college”

Regression analysis

Regression analysis (Table 3) indicated that when controlling for age, education, income, race, and vaccine access, political conservatism and conservative regional climate were positively associated with vaccine hesitancy (β = .23, p < .001; β = .11, p = .001; respectively). Additionally (Table 4), the interaction between political conservatism and conservative regional climate was significant (β = .09, p = .003)(Table 3, Fig 2). As shown in Table 3, political conservatism and conservative regional climate were also positively associated with booster hesitancy (β = .18, p < .001; β = .11, p = .002; respectively) and the interaction between these two variables with booster hesitancy was significant (β = .09, p = .003)(Table 4, Fig 2). As shown in Table 5, political conservatism and conservative regional climate were negatively associated with vaccine status [odds ratio (OR) =.68, p < .001; OR =.72, p = .004; respectively]. The interaction between political conservatism and conservative regional climate (Table 6) was not significant (OR = 1.02, p = .833). As shown in Table 5, political conservatism and conservative regional climate were negatively associated with booster status (OR =.82, p = .022; OR =.77, p = .004; respectively). Additionally, there was a significant interaction (Table 6, Fig 2) between political conservatism and conservative regional climate (OR =.83, p = .026). Results for all regression models with control variables can be found in Tables A-D in S4 File.

Table 3. Linear regression testing for main effects of political conservatism and conservative regional climate on vaccine hesitancy and booster hesitancy, United States, 2023.

Variable	Vaccine Hesitancy (n = 664)			Booster Hesitancy (n = 664)
Variable	β	95% CI	p	β	95% CI	p
Political Conservatism	.22	.16,.28	<.001	.18	.11,.24	<.001
Conservative Political Climate	.11	.05,.18	.001	.11	.04,.19	.002

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Note: Both models controlling for age, gender, income, education, race, and vaccine access

Table 4. Linear regression testing for main effects of and the interaction between political conservatism and conservative regional climate on vaccine hesitancy and booster hesitancy, United States, 2023.

Variable	Vaccine Hesitancy (n = 664)			Booster Hesitancy (n = 664)
Variable	β	95% CI	p	β	95% CI	p
Political Conservatism	.23	.23,.35	<.001	.18	.12,.25	<.001
Conservative Political Climate	.11	.03,.17	.002	.11	.04,.18	.003
Conservatism * Conservative Climate	.09	.00,.13	.003	.11	.04,.17	.003

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Note: Both models controlling for age, gender, income, education, race, and vaccine access.

Fig 2 — Four graphs depicting interactions between political conservatism and regional political climate for booster hesitancy, vaccine status, and booster status.

Table 5. Logistic regression for main effects of political conservatism and conservative regional climate on vaccine status and booster status, United States, 2023.

Variable	Vaccine Status (n = 664)			Booster Status (n = 664)
Variable	OR	95% CI	p	OR	95% CI	p
Political Conservatism	.68	.54,.84	<.001	.83	.69,.99	.034
Conservative Political Climate	.73	.58,.90	.004	.76	.63,.92	.002

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Note: Both models controlling for age, gender, income, education, race, and vaccine access.

Table 6. Logistic regression testing for main effects of and the interaction between political conservatism and conservative regional climate on vaccine stauts and booster status, United States, 2023.

Variable	Vaccine Status (n = 664)			Booster Status (n = 664)
Variable	OR	95% CI	p	OR	95% CI	p
Political Conservatism	.68	.54,.84	<.001	.82	.69,.98	.022
Conservative Political Climate	.72	.58,.90	.004	.77	.63,.93	.004
Conservatism * Conservative Climate	1.02	.82, 1.27	.833	.83	.69,.99	.026

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Note: Both models controlling for age, gender, income, education, race, and vaccine access.

Discussion

The present research provides the first assessment of the potential interaction between political orientation, political climate, and COVID vaccination. Additionally, by looking at both COVID vaccine and booster status and hesitancy, we identify how attitudes and behaviors may have shifted as we transitioned from initial vaccine doses to boosters. Taken together, our findings indicate that political climate, as represented through regional 2020 presidential election votes, has a stronger association with vaccine and booster hesitancy for individuals who are higher on political conservatism as compared to those who are politically liberal. This same trend was observed for self-reported booster status, but not self-reported vaccine status. Most strikingly, for booster status the interaction between political orientation and political climate indicated that the most liberal participants were over 65% likely to get boosted regardless of climate. The most conservative participants in liberal climates were 60% likely to get boosted, while conservatives in conservative climates were less than 35% likely to get boosted (OR =.83, p = .026).

Political conservatism and conservative regional climate were both associated with higher levels of COVID-19 vaccine and booster hesitancy and lower rates of self-reported vaccination and boosting, when controlling for age, education, income, and race. Additionally, there was a significant interaction between political conservatism and conservative regional climate for vaccine hesitancy, booster hesitancy, and booster status. The interaction for vaccine hesitancy indicates that at the lowest levels of conservatism (i.e., liberalism) vaccine hesitancy is lower regardless of regional climate. As political conservatism increases vaccine hesitancy also increases, but this increase is even greater for individuals who live in a more conservative regional climate. There was a similar pattern for booster hesitancy, with more politically conservative individuals in a conservative regional climate having higher booster hesitancy than conservative individuals in a liberal regional climate. For the most politically liberal individuals, booster hesitancy was slightly lower in more conservative regional climates. The interaction for self-reported booster status was consistent with those for vaccine and booster hesitancy. Booster status decreased in likelihood as political conservatism increased. There was no difference in the likelihood of reporting receiving a booster based on conservative regional climate for the most politically liberal participants, but as political conservatism increased, the likelihood of receiving a booster was lower for participants in a conservative regional climate as compared to those in a liberal regional climate. The interaction for vaccination was not significant, this difference between attitudes (hesitancy) and action (vaccination), likely reflects the high prevalence of COVID-19 vaccine mandates. Our research is consistent with previous findings that conservative political orientation and a conservative political climate are both associated with COVID-19 vaccine hesitancy and decreased likelihood of receiving a COVID-19 vaccine [6,7,10,19]. Previous studies have focused on either political orientation or political climate. By looking at political orientation and region climate, we identify a novel and important finding, specifically that conservative individuals are more likely to received booster doses in liberal regions. While we did not asses the mechanism through which this occurs, these findings suggest that environmental factors (e.g., social pressure) may impact decisions to receive annual COVID-19 doses.

Because we used 2020 presidential election votes by zip code as a proxy for political climate, it must be noted that other zip code-based differences could be driving the associations noted in this paper. Regional factors (e.g., population density) may play a role in linking 2020 election votes to vaccine attitudes and behaviors. While our analyses controlled for demographic covariates commonly associated with vaccine hesitancy, we did not formally assess effect modification by these factors. Consequently, the reported pooled associations should be interpreted as average effects across the sample, and we cannot exclude the possibility that heterogeneity across demographic subgroups could produce aggregation effects such as Simpson’s paradox. Another limitation in this dataset is the reliance on self-reported vaccine status. Self-report is susceptible to social desirability [30] and recall bias [31], and we cannot assume that reported vaccine status reflects actual vaccine status. Additionally, our sample was recruited through an online platform and only includes participants with access to this platform. Further, our sample includes only White and Black participants recruited through Qualtrics Panels, and may not generalize to broader populations in the United States. Finally, it should be noted that this data is cross-sectional and we cannot assume causal or directional associations. Despite these limitations, this research provides novel evidence that political climate in the US is associated with vaccine hesitancy and uptake for politically conservative individuals.

Identifying the specific mechanisms through which this happens would have significant implications for increasing vaccine uptake among politically conservative individuals across the US. The interaction between political climate and orientation suggests that liberal environments may exert more influence on conservative individuals than conservative environments exert on liberal individuals. It could also be the case that conservative individuals are more influenced by their environments than their liberal counterparts. Future research should explore the extent to which political climate, and other related regional variables, are associated with vaccine attitudes and behavior. Regardless, these findings are critical to our understanding of the association between political orientation on vaccine hesitancy. These results have significant implications for populations to target to increase COVID-19 vaccine uptake as we move towards annual vaccinations.

Supporting information

S1 File. Items from Life Experiences and COVID-19 – Qualtrics.

(DOCX)

pone.0342063.s001.docx^{(21.1KB, docx)}

S2 File. Confirmatory Factor Analysis.

(DOCX)

pone.0342063.s002.docx^{(15.9KB, docx)}

S3 Table. Participant Distribution by State.

(DOCX)

pone.0342063.s003.docx^{(18.6KB, docx)}

S4 File. Results for All Regression Models with Control Variables.

(DOCX)

pone.0342063.s004.docx^{(29.9KB, docx)}

Data Availability

The materials and data presented in this study are openly available in OSF at https://osf.io/s2knw/?view_only=4de7d7296d0a44acab11174e357776b.

Funding Statement

This research was funded by a Public Affairs and Policy Research Initiative Grant from Colgate University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

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PLoS One. doi: 10.1371/journal.pone.0342063.r001

Decision Letter 0

Mickael Essouma

17 Sep 2025

Dear Dr. Kmush,

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.

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5. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Additional Editor Comments:

I. Major comments

-About the Introduction. I find it quite long. In my opinion, you would better start by highlighting the burden of COVID-19 in USA up to the end of the study period (in terms of morbidity and case fatality), a brief history of the introduction of COVID-19 vaccination in USA, and how COVID-19 vaccination influenced the burden of COVID-19 in USA based on the most compelling evidence; while also highlighting the scientific controversy surrounding the efficacy of COVID-19 since their approval (see Monash Bioeth Rev. 2024 Jun;42(1):28-54. doi: 10.1007/s40592-024-00189-z and https://jamanetwork.com/journals/jama-health-forum/fullarticle/2836434). You could then transition to a second paragraph to show that the controversy spread beyond the scientific milieu to reach the politics and the public, providing the overall viewpoint of the public and political agents about COVID-19 vaccine. This is where the authors would provide updated knowledge and knowledge gaps about the contribution of politics (notably political conservatism) to COVID-19 vaccine hesitancy among them and among the entire US population, and would describe how conservatist political ideologies, political climate and the current US president help shaped the narrative and trust around COVID-19 vaccine efficacy in political milieus and in the general US population. This would help the authors usher into the overarching goal of this study and the aim of the study taking into consideration the fact that COVID-19 vaccine efficacy or their life-saving potential is still being actively discussed among health community members , political agents and the general public. With editing efforts, this could lead to a-1.5 page introduction.

-About the Materials and Methods section. I suggest starting this section with a sub-section termed «Study design» where you would state that this was «an online behavioral study using the Qualtrics panel». After that, you would explicitly state that you conducted and reported this study according to a guide for psychological research based on data panel (current ref 26). This would help better understand the rest of the section and enhance the credibility of the study report. I am unable to access ref 26 full text and so, I do not know whether there is a checklist in that guide that you should eventually consider to fill and upload as supplemental Material to showcase the compliance with ref 26 guidelines. In line 85, you stated that participants were recruited from USA and zip codes seem to have been important variables when going through the PROCEDURES sub-section. Could you then go into more details with the geographic description of participants' living places in line 85: specific states where the study was conducted, in urban vs rural areas, in which zip code area, while explaining what zip codes represent in USA and perhaps their corresponding variables outside USA for international readers? Along with the suggestion to describe Qualtrix panels when mentioning the study design, consider completing the information in the sentence "Qualtrix panel...income" (lines 87 and 88) with specification in the PARTICIPANTS sub-section of further explicit information about the inclusion and exclusion criteria which are somewhat unclear in lines 88-94? For instance, what age, sex and gender groups did you include? What political ideology? What about COVID-19 vaccination status? Did you exclude individuals with neurocognitive deficiencies given that they may affect data quality (see "Data quality of platforms and panels for online behavioral research | Behavior Research Methods" https://link.springer.com/article/10.3758/s13428-021-01694-3)? Why did you include only two racial groups? What about participants'ethnic groups? What guided the choice of the income limit for participants? Did you include some zip codes to target particular economic groups of participants? What sampling method did you use for participants selection? How did you estimate the sample size? Could you add a "Data items or Variables" sub-section in between the PARTICIPANTS and PROCEDURES sub-sections to clearly describe the Qualtrix panel data used in this study with the variables (e.g., participants' sociodemographic and economic variables, political conservatism and its modalities, political climate and its modalities, and COVID-19 vaccine variables)? That description needs to be accompanied by a QUALTRIX questionnaire template used uploaded as a supplemental material. Consider reorganizing the current PROCEDURES sub-section to more clearly and deeply describe each of the research procedures carried out, except for statistical analysis which is described in a specific sub-section after the PROCEDURES sub-section: protocol preparation and registration, getting ethical matters sorted, participant enrolment (including the spread of questionnaires online, information of the target population and getting participants' responses), collection of survey responses/data, data storage before analysis (and eventually exportation towards the statistical analysis software). Could you complete the sentence in lines 96 and 97 with the IRB reference number? Given the inclusion of only two racial groups, can you claim to have complied with the recommendation for diversity and inclusivity when selecting study participants as mentioned in the 2024 Declaration of Helsinki? See line 97. This issue should be mentioned in the limitations statement of the Discussion section when commenting on the generalizability of study findings. The sentence in lines 98-100 would better be moved to the PARTICIPANTS sub-section, and the study period would better be mentioned at the beginning of the Materials and Methods section. I suggest adding a sub-section for the definition of operational terms (political conservatism, political climate, and COVID-19 vaccine terms) in between the PROCEDURES and STATISTICAL ANALYSIS sub-sections. The STATISTICAL ANALYSIS sub-section also needs more precision about data curation and quality control. Could you be more specific about the type of logistic regression performed for the main association analysed? What were the independent and dependent variables? How did you select the covariates? Along this line, it seems that keeping selecting race as a covariate (as did the authors) in association analyses could contribute to persistence of racial inequities in social (notably health) sciences research: see "“We adjusted for race”: now what? A systematic review of utilization and reporting of race in American Journal of Epidemiology and Epidemiology, 2020–2021 | Epidemiologic Reviews | Oxford Academic" https://academic.oup.com/epirev/article/45/1/15/7288093?login=false. Did you perform sensitivity analyses? Could you clearly explain how the moderation analysis mentioned in the manuscript's title was performed and why moderation analysis was selected over mediation analysis? How did you determine the effect magnitude estimates and their 95 percent confidence intervals? Why did you use both PR and OR as effect magnitude estimates? How do you present data in the text and in illustrations (e.g. how were tables and figures drawn, stratified/unstratified)? The sentence "For all analyses, ...from the analysis." in lines 147 and 148 belongs to the PARTICIPANTS sub-section.

-About the Results section. It is difficult to read, with no sub-section. I suggest the first sub-section on participants' social, demographic and economic characteristics. The second sub-section should correspond to the association assessed, and the third one to results of the moderation analysis. Could you highlight the process of participant selection in a figure?

-About the Discussion section. Consider first summarizing the results, then interpreting them, then the limitations and strengths statements, and finally, recommendations for policy makers, researchers, and clinicians.

II. Minor comments

-Amend the abstract, keywords, conclusion and references as required, and conform to PLOS One author guidelines when formatting the revised manuscript.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

Reviewer #4: Partly

Reviewer #5: Partly

**********

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

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: No

**********

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

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

Reviewer #1: Dear Author(s),

This manuscript presents a timely and original investigation. The study is methodologically rigorous, with clearly defined research objectives, appropriate statistical techniques, and a transparent presentation of results. The analyses are carefully executed and align with the study’s stated hypotheses, with the findings logically supporting the conclusions.

The introduction does a commendable job of situating the study within the existing literature, but the framing could be strengthened by more explicitly highlighting the novelty of this work in comparison to prior studies. While the discussion section interprets the results appropriately, it could benefit from more direct linkage to potential practical implications and a clearer acknowledgment of the limitations beyond those already noted. This would help readers contextualize the findings for both academic and applied settings.

The statistical methods are robust and well-documented. It is particularly positive that the data are made fully available, ensuring reproducibility. Figures and tables are clear, relevant, and appropriately labeled, though a few captions could be expanded to ensure they are fully interpretable without referring back to the main text. The manuscript is written in clear, standard English, with only a few minor grammatical refinements needed to further enhance readability.

In short, this is a solid, technically competent study that advances understanding in its domain. The suggested revisions are relatively minor and focus on enhancing clarity, framing, and interpretive depth rather than making substantive analytical changes.

Best regards,

Reviewer #2: I. SUMMARY

This manuscript examines the relationship between individual political orientation, local political climate, and COVID-19 vaccine hesitancy. While the topic is of potential public health and sociopolitical interest, the manuscript suffers from fundamental flaws in conceptual framing, theoretical grounding, and scientific value. These weaknesses render its conclusions essentially meaningless, regardless of what the statistical output happens to show.

1. Premise and Scientific Merit

The central premise — that political orientation and political climate are associated with COVID-19 vaccine hesitancy — is already well-documented to the point of redundancy. This work does not substantially advance that knowledge. Instead, it repackages an established correlation without offering:

- New explanatory mechanisms that could be tested or falsified.

- Nuanced evaluation of why hesitancy exists in various populations.

- Consideration of whether the hesitancy might be rational under certain conditions.

A central tenet of scientific inquiry is that the question itself must have potential to generate meaningful or actionable insight. This study does not meet that threshold; even if flawlessly executed, it could not produce findings that alter scientific understanding or public health practice.

2. Lack of Engagement with Intervention Efficacy

The manuscript treats COVID-19 vaccination as a universally optimal intervention and implicitly assumes that increased uptake is inherently desirable. There is no attempt to evaluate the real-world efficacy, safety, or cost-benefit profile of the intervention in question at the time of data collection. This omission is not merely a gap in discussion — it invalidates the interpretive frame. Without assessing whether the behavior being measured is objectively beneficial, the act of labeling hesitancy as problematic is scientifically hollow.

3. Theoretical Weakness

The introduction attempts to invoke concepts such as group polarization and resistance to persuasion, but these are deployed as rhetorical devices rather than as elements of a coherent, testable model. There is no operational definition of how these mechanisms would be detected, nor any consideration of alternative models that could explain the same data. The result is a literature review in service of a foregone conclusion, rather than a genuine hypothesis-building exercise.

4. Methodological Concerns

Measurement validity: The political climate variable is a blunt instrument (% Republican vote by ZIP code) that may capture a variety of demographic, cultural, and socioeconomic factors unrelated to political ideology per se. These are neither acknowledged nor controlled for.

Scale modification: The Attitudes towards Adult Vaccination Scale was altered for COVID-specific use without any psychometric re-validation, raising concerns about reliability and construct validity.

Exclusion handling: Participant attrition is reported piecemeal, making it difficult to follow the data pipeline from initial sample to final analytic N.

5. Interpretive Overreach

Correlational results are treated as if they illuminate underlying cognitive or epistemic deficiencies in certain political groups. No evidence is provided to support such inferences, and alternative explanations (e.g., differing trust in institutions, differential access, or varying personal risk assessments) are not meaningfully considered. This transforms the manuscript from a descriptive study into an exercise in political attribution, which is outside the scope of empirical demonstration here.

6. Contribution to the Field

The manuscript’s contribution is minimal. It offers no novel methodology, no theoretical innovation, and no actionable public health insight. Similar results have been repeatedly reported in both academic and popular media outlets since 2020. In its current form, this work risks functioning more as political commentary than as science.

II. DETAILED REVIEW

A) INTRO

"In the US, political conservatism has been consistently associated with greater COVID-19 vaccine hesitancy (1–7) and lower COVID-19 vaccine uptake (5,8,9)"

Comment: Opening with an already well-established, heavily publicized correlation signals this study is unlikely to produce novel insight. If the field already accepts this as a settled empirical fact, the manuscript must immediately justify why retesting it is scientifically useful. No such justification follows.

"While political conservatism has been historically associated with lower trust in the scientific community..."

Comment: This “historically associated” claim is sweeping and imprecise. No operational definition of “trust in the scientific community” is given here, and the citations do not establish causality. This language sets up a simplistic cause-effect chain without acknowledging other mediating variables.

"...the increased partisan divide in vaccine hesitancy may be attributed, at least in part, to the politicization of COVID-19 during the Trump administration (12)"

Comment: This is a politically charged attribution offered without empirical demonstration in this study. Even if true, it is a claim about historical causation that cannot be substantiated by the present dataset.

"President Donald Trump initially downplayed the COVID-19 pandemic, even labeling it as a hoax (13)..."

Comment: This paragraph functions rhetorically, not scientifically. The detail is not necessary to establish the study’s variables, and its inclusion risks signaling partisan bias. Its presence here does nothing to clarify hypotheses or inform operationalization.

"Nonetheless, the influence of political orientation on vaccine hesitancy has been increasing..."

Comment: Again, this reiterates the opening claim. The intro has now made the same “conservatives = more hesitant” point three times in different words, with no additional conceptual depth. This redundancy inflates length without adding value.

"There is an emerging body of evidence that the broader political climate..."

Comment: This is a potentially interesting angle (regional climate effects) but is described in the same correlational terms as before. No competing hypotheses are introduced, no mechanisms proposed beyond “politics affects attitudes,” and no argument for why this is worth re-examining in 2023.

"While previous research has identified the unique impact of both political orientation and political climate...little research on the interaction..."

Comment: This should have been the opening sentence. It is the closest the introduction comes to stating a gap in the literature — but it arrives buried after several paragraphs of repetitive background and political editorializing.

"Group polarization...could lead those who are politically conservative and live in politically conservative areas to have even stronger vaccine hesitancy..."

Comment: This is a generic invocation of a social psychology concept without operational definition. How is “group polarization” measured here? How does the design distinguish polarization from simple majority influence? This is theory-name-dropping, not hypothesis development.

"For example, self-identified Republicans reported greater intention to vaccinate after watching a video of Trump..."

Comment: The example is selectively chosen to reinforce the prior partisan framing. It is anecdotal in the context of the argument and does not advance the operationalization of the current study’s variables.

"In this way, when conservative individuals...the group polarization effect is created..."

Comment: This is an assumption stated as fact. The present study does not measure interpersonal influence, exposure to like-minded views, or any group-level dynamics. “Group polarization” is being asserted as the mechanism without evidence.

"Individuals who are exposed to positive vaccine messaging...we might expect that conservative individuals who live in liberal areas..."

Comment: The shift here from conservative-in-conservative-areas to conservative-in-liberal-areas feels unfocused. The introduction now contains multiple, partially contradictory speculations without clearly stating the testable predictions or expected interaction patterns.

"Alternatively, resistance to persuasion is a well-documented psychological phenomenon..."

Comment: This is a second, competing explanatory frame, also undeveloped. The introduction now lists two incompatible mechanisms without specifying which is predicted to dominate or under what conditions.

"Here, we examined the moderating role of political climate on the association between individual political orientation and COVID-19 vaccine hesitancy."

Comment: This is the first clear statement of the research aim — and it appears at the end of the introduction. By this point the reader has waded through redundant background and partisan color commentary that could have been condensed into three sentences.

B) METHODS

i) Participants

"Participants were recruited from the United States by Qualtrics Panels(26)"

Comment: Opt-in online panel samples are inherently self-selected and not probability-based. This is fine for exploratory work but disqualifies the study from making strong generalizations about “Americans” as a whole. No acknowledgement of this limitation appears here.

"The data and analysis presented in this paper are part of a larger project..."

Comment: This raises immediate concerns about data mining. Without a preregistered analysis plan, the “larger project” could have tested multiple variables, cherry-picking the political–vaccine link for publication. No declaration of how this study was scoped relative to the larger dataset.

"Qualtrics Panels recruited participants across four quota groups based on race and income..."

Comment: The quota sampling is not stratified random sampling — it’s cosmetic demographic balancing. The chosen quotas (White/Black, above/below median income) are arbitrary with respect to the stated research question and introduce unnecessary complexity that is never analytically leveraged.

"...no more than 60% of the participants be of any gender."

Comment: Again, quota design is arbitrary. Gender balancing is fine, but this appears to be a procedural checkbox, not a hypothesis-driven decision.

Procedures

"Political conservatism was assessed across two items... rated 0 to 10... averaged to form a political conservatism scale."

Comment: The measurement is narrow — “conservatism” is inferred from only two self-report sliders on social and economic issues. No psychometric validation is cited for collapsing these into a single score, nor is the correlation between the two items reported. This invites construct validity concerns.

"Conservative regional climate... calculated from 2020 presidential election results... % Republican vote by ZIP code."

Comment: This variable is a blunt proxy. Political climate is collapsed into a single presidential vote share metric, ignoring voter turnout rates, third-party votes, temporal changes between 2020 and survey collection in 2023, issue-specific political leanings that might diverge from presidential voting. As well, no control for confounding geographic factors (urban/rural, education levels, local COVID policy intensity) is attempted, making causal inference impossible.

"In cases where a zip code included multiple FIPS codes, data was averaged..."

Comment: Averaging election returns across multiple FIPS codes further dilutes the precision of the “political climate” measure. This method produces an ecological variable with unknown reliability.

ii) Vaccine and Booster Measures

"Self-reported vaccine status..."

Comment: Self-report is fine for some measures, but no attempt is made to validate these reports or assess recall/social desirability bias. Given the politicization of the topic, measurement error here is a real threat.

"Vaccine hesitancy was measured using the seven items from the Attitudes towards Adult Vaccination Scale"

Comment: Major validity problem — the scale was altered (wording changed to COVID-specific) with no psychometric re-validation. The authors cannot assume reliability or factor structure remains intact post-modification. This is particularly problematic given they use the scale as a primary outcome.

iii) Statistical Analyses

"Pearson correlations... t-tests... logistic regression models... controlling for age, gender, education, income, and race."

Comment: The control variables are minimal and do not include plausible covariates such as local COVID rates, occupation type, comorbidities, or trust in specific institutions. The omission of these makes any claim of “political orientation causes hesitancy” statistically meaningless.

"Any participants with missing data on any of these variables were excluded from the analysis."

Comment: This is listwise deletion without justification. No exploration of whether missingness is random, nor any multiple imputation approach. This risks biasing the sample further toward respondents with complete, possibly non-representative data.

"Two-sided p-values less than 0.05 were considered statistically significant."

Comment: Standard threshold — but there’s no discussion of multiple comparison correction despite running numerous models and correlation tests. This inflates Type I error risk, meaning “significant” results could be statistical noise.

C) RESULTS

"Qualtrics Panels collected data from 1777 participants, and eliminated 970 participants... resulting in a sample of 798 validated participants."

Comment: Losing over half the initial sample to attention check failures or incomplete surveys is a bright red flag for data quality. This isn’t just “cleaning” — it’s an indication that the recruitment pool was not engaged or representative. Attrition on this scale guts external validity.

"We eliminated an additional 115 participants who did not provide valid zip code data or whose zip code did not have corresponding election data."

Comment: That’s another ~14% gone. You’re now down to 38% of the original sample. At this point, you’ve essentially self-selected for respondents willing to fully disclose location data and complete a long survey — exactly the sort of filtering that will exacerbate ideological skew. No discussion of this bias appears.

"Participants ranged in age from 18 to 94 years"

Comment: No indication of how age distribution compares to the general US population — again, no representativeness check.

"519 of participants reported receiving the initial COVID-19 vaccine and 365 reported receiving at least one COVID-19 booster."

Comment: These uptake rates are far lower than CDC national estimates at comparable time points, suggesting a non-representative sample. Without weighting or adjustment, any national-level inferences are invalid.

"Both vaccine hesitancy and booster hesitancy scores ranged from 1 to 5..."

Comment: They report range, mean, SD — but still no check of scale reliability after modification. Without that, these are just arbitrary composites of unvalidated items.

"the % Republican vote by zip code ranged from..."

Comment: Reporting range and SD doesn’t fix the problem that this metric is stale (from 2020) and ecologically coarse.

"Vaccine hesitancy was positively correlated with booster hesitancy"

Comment: r = .86 simply means the two “different” hesitancy scales are basically measuring the same construct — not surprising since they’re word-substitution versions of the same unvalidated instrument. This is tautology masquerading as insight.

"...political conservatism and conservative regional climate"

Comment: r = .36 is a modest correlation; r = .16 is trivially small. With N = 683, even trivial associations will be “statistically significant” — but they’re explaining barely 2–13% of the variance. These are the sorts of effects that evaporate in better-controlled designs.

"Unvaccinated participants were significantly younger... "

Comment: This is pure confirmation of what was baked into the premise. The model is underspecified — “political conservatism” is correlated with multiple sociodemographics here, any of which could be the actual driver. Without multivariate disentangling of collinearity, these t-test “findings” are little more than descriptive stereotypes.

"Similarly, participants who reported not receiving a booster..."

Comment: This is a repeat of the vaccine status pattern, just with boosters. Again, no attempt to adjust for overlapping covariates beyond a few controls in later regressions. The paper is making the same point twice and treating it as separate evidence.

Everything here is correlational and ecologically confounded. Yet the narrative implicitly treats “living in a conservative climate” as an independent driver of hesitancy, without testing for other area-level variables (e.g. education rates, healthcare access, prior infection rates), or establishing temporal ordering (i.e. did political climate cause hesitancy, or did both stem from a third factor?).

What the data actually shows: in an opt-in, quota-balanced panel surveyed in spring 2023, self-reported COVID vaccination and booster uptake correlate modestly with a 2-item conservatism index and trivially with 2020 presidential vote share aggregated to ZIP codes.

What the data does not show: causation, mechanisms (polarization/persuasion), efficacy of any intervention, or meaningful predictive power beyond generic demographics.

Nothing actionable can be inferred from this work without further experiments, better measures, and serious controls.

III. CONCLUSION

The manuscript’s central premise — that political orientation and local political climate are associated with COVID-19 vaccine hesitancy — is already well established in both the academic and popular literature. The study offers no new theoretical model, explanatory mechanism, or intervention test. Even if the analyses were flawless, the research question is scientifically low-yield and cannot advance understanding or public health practice.

Sampling included arbitrary quotas that are unrelated to the stated research aim. Attrition was extreme, raising concerns about bias and representativeness. Key measures suffer from construct validity issues. Control variables omit plausible confounders (local COVID burden, healthcare access, comorbidities, trust in public health). Missing data were handled by listwise deletion with no missingness analysis. No multiple-comparison correction was applied despite numerous tests.

The findings are predictable from the operational definitions: modest correlation between self-reported conservatism and hesitancy and trivial correlation with political climate. Booster hesitancy and vaccine hesitancy correlation indicates redundancy rather than independent confirmation. Attrition and non-probability sampling preclude generalization; no weighting or sensitivity analyses are reported. The “climate” effects are statistically significant only due to sample size and would likely vanish under stronger controls.

The Discussion implicitly treats observed correlations as evidence of causation and invokes “group polarization” without having measured any mechanism (social networks, media exposure, interpersonal influence). Causal language is applied to variables measured cross-sectionally, ecologically, and with unvalidated instruments. Hesitancy is framed as inherently problematic without assessing the contemporaneous efficacy/risk profile of the intervention (vaccination in early 2023), reducing the interpretation to a value judgment rather than a scientific conclusion.

Policy implications are speculative and unsupported — no interventions were tested, yet recommendations for targeted messaging are offered. Sampling and measurement limitations are not adequately engaged, and effect sizes are ignored in favor of p-value significance.

Reviewer #3: The manuscript examines a salient moderating role of regional political climate between self-reported political tendency and vaccine (or booster) hesitancy. I would like to kindly suggest several robustness-checks on the key variable of this study, regional political climate. The author(s) carefully built the variable at the zip code level which is geographically granular, potentially reflecting on neighborhood-level political atmosphere. However, maybe a more macro-level data such as state can better capture political climate in which a respondent resides. So it would be great to use state-level 2020 election results instead of zip code-level data as a robustness check. This especially makes sense because the state government led most responses to the COVID-19 pandemic, rather than zip code-level local responses. In the same context, an additional table on where the 683 respondents live state by state will be informative and important for readers because some readers may raise a question of "what if most of the 683 respondents live in California? Or Texas?" An additional supplemental table may help clarify this natural question. More importantly, a basic question would be if and the extent to which political climate differs across zip code areas. Is there a huge variation across adjacent zip code areas within the same county or state? Or are they nearly same to each other within the same county or state? This evidence would be important when the author(s) justify WHY they use zip code level variable. Hope these comments be able to help improve the manuscript. Thank you very much.

Reviewer #4: I am writing to provide a critical evaluation of the manuscript entitled “Political Conservatism, Political Climate, and COVID-19 Vaccination Hesitancy and Uptake in the United States.” The paper analyzes a Qualtrics Panels survey administered in March–April 2023 to assess how local political climate conditions the association between individuals’ political conservatism and both COVID-19 vaccine hesitancy and booster uptake. The central empirical claim is that liberals are consistently less hesitant regardless of place, whereas conservatives report lower hesitancy and higher booster uptake when they reside in more liberal political climates. The authors estimate linear models for attitudinal indices and logistic models for uptake, focusing on a conservatism × local vote-share interaction while adjusting for standard demographics.

The question is important and timely. Since the first year of vaccine rollout, independent population-level sources have documented unusually strong associations between vaccination and partisan voting patterns, far exceeding analogous correlations for seasonal influenza; situating the manuscript explicitly against this backdrop will help readers interpret the magnitude and direction of the reported effects.

The paper’s contextual moderation claim also aligns with two complementary literatures: work showing that the political composition of interpersonal networks predicts vaccine confidence and behavior, and classic theories of social influence and cross-pressures indicating that prevailing local norms can dampen or amplify individual predispositions. In addition, experimental evidence has shown that elite partisan cues can increase vaccination intentions among Republicans, underscoring the plausibility of the mechanisms the authors invoke while also highlighting the difficulty of distinguishing interpersonal from elite informational channels in observational designs.

The data and design are generally appropriate for the questions posed, and the authors deserve credit for applying response-quality screens typical of online panel work. That said, two measurement decisions warrant revision. First, the hesitancy construct is adapted from existing adult-vaccination items but reworded for COVID-19. Best practice requires reporting fresh psychometric evidence when items are modified or redeployed to a new context. Following COSMIN guidance, the manuscript should document internal consistency (e.g., α or ω), item–total correlations, dimensionality (CFA/parallel analysis), and, given the paper’s focus, measurement invariance across key subgroups such as party identification. Second, the “political climate” measure is built by assigning 2020 presidential vote shares to respondents’ ZIP codes using crosswalks. Because USPS ZIPs are not stable statistical areas, and Census ZCTAs only approximate them, such crosswalks can induce nontrivial boundary and smoothing error. I recommend sensitivity analyses at alternative geographic units (ZCTA, county) and discussion of potential misclassification introduced by ZIP–ZCTA conflation.

The statistical specification is serviceable but can be improved in ways that would materially strengthen the inferences. First, because the key moderator is a place-based attribute, standard errors should be clustered geographically or, better, modeled with multilevel structure to reflect shared context and avoid overstated precision. A multilevel framework also enables partial pooling across places and principled estimation of cross-level interactions; the canonical reference remains Gelman and Hill.

Second, given the use of a nonprobability online panel, I encourage the authors to report weighted estimates or apply multilevel regression and post-stratification (MRP) to align sample margins to ACS or CPS benchmarks; both the AAPOR task force and subsequent methodological work have set clear expectations here. Third, because political climate is correlated with structural covariates—urbanicity, race composition, and socioeconomic status—the models should incorporate ecological controls (e.g., rural–urban continuum codes or vulnerability indices). CDC surveillance has consistently shown lower COVID-19 vaccination in rural counties, a pattern relevant to interpretation of any contextual moderation.

A few issues of clarity and transparency also deserve attention. The abstract and methods alternate between odds ratios and “PR,” suggesting a prevalence-ratio estimator for booster outcomes; if Poisson regression with robust variance was used, this must be stated explicitly and applied consistently, with 95% confidence intervals for all effect measures. The moderation results would benefit from covariate-adjusted marginal-effects plots, presented alongside the distribution of the moderator to avoid extrapolation beyond observed ranges. Because the observed period corresponds to early uptake of the bivalent booster—when overall adult coverage remained modest and varied across groups—anchoring the manuscript’s descriptive statistics against CDC population estimates would enhance external validity and give readers a concrete sense of scale.

Interpretively, the manuscript’s conclusions are plausible and are framed in a manner consistent with the literatures noted above. Yet causal language should be tempered. Individuals do not randomly sort into political contexts; the interaction between ideology and place could reflect unmeasured selection or access channels (e.g., provider availability, mandate environments) rather than pure normative pressure. To probe this, the authors might add robustness checks using alternative geographies; introduce proxies for local information environments; and, if feasible, re-estimate the moderation in multilevel models that include area-level covariates. Finally, a brief “measurement appendix” documenting the exact wording of the adapted items, response options, and scale properties would meet contemporary reporting standards for patient-reported measures and reduce concerns about construct drift.

In sum, the manuscript addresses a consequential and theoretically rich question and presents evidence consistent with the notion that local political climates condition the effect of individual conservatism on vaccine hesitancy and uptake. With revisions that (i) document the adapted scale’s psychometrics in line with COSMIN recommendations; (ii) reconcile and standardize effect measures for booster outcomes; (iii) adopt clustered or multilevel inference and appropriate post-stratification or MRP for generalization; and (iv) demonstrate robustness to alternative spatial operationalizations of political climate while acknowledging ZIP/ZCTA limitations, the paper would make a valuable contribution to the study of politically charged health behaviors. Given the importance of these methodological and reporting improvements, my editorial recommendation is at least “minor revision.”

Reviewer #5: There is a lot to like about the manuscript. Vaccine hesitancy is an interesting topic that should have broad appeal. The article is well written, both because it is easy to read and because it is careful with language. The core finding is interesting: conservatives who live in places with more liberals are less hesitant to be vaccinated for COVID-19.

However, I have some serious reservations about the rigor of the science. To me, the study seems like it is better suited to be a piece of a research paper---not a stand alone project. There is too much data and information missing to make an inference like the authors claim. I cannot recommend publication, but I think more work to explore these relationships will develop the piece into a solid publication. My major concerns boil down to sampling, attrition, and measurement.

Sampling: The authors start out with a little less than 2000 adults in a their survey. The authors study persons who identify as only White, or only Black. In doing so they limit themselves to a majority population and one minority population, both of which are shrinking as a proportion of the United States. (See https://www.census.gov/library/stories/2021/08/improved-race-ethnicity-measures-reveal-united-states-population-much-more-multiracial.html) While they represent the majority of the population, they are a poor selection for understanding the difference between conservative and liberal parts of the United States. The sample likely represents conservatives much better than it does liberals. It is missing multi-racial persons, the fastest growing population in the United States, and its missing Hispanics and Asians. All three of these groups are more likely to identify as Democratic or lean Democratic (See https://www.pewresearch.org/politics/2024/04/09/partisanship-by-race-ethnicity-and-education/) and black Americans are poor proxies for these groups. Controlling does not help with the fact of all together missing 25% of the population.

Attrition: The authors lose two thirds of their survey population because they fail to answer attention questions correctly or key questions in the survey. This is missing data that may very well bias results. Given the magnitude it may make relationships that are significant seem to not be. The authors do not tell us if the sample still balances on income and race. The authors also do not tell us how attrition is related to their moderator or if it is spatially correlated. These relationships all need to be explored. Other data may be needed to clarify the impact of the unfortunately high attrition rate.

Measurement: The authors conflate living in a liberal area with living in an area where most people voted for Biden and living in a conservative area with living in an area where most people voted for Trump. While these are no doubt overlapping categories, it's not obvious that this voting snapshot doesn't account for the results. It may be that many of the 'conservatives' that live in Biden voting areas, actually live in areas that keep on switching. These places are hardly the same as the densely populated counties that comprise the majority of Democratic votes. Robustness is necessary. The authors might use other elections like gubernatorial elections or senate elections. They could also use measures like the moral values measures that Ben Enke uses.

A final note the FIPS-ZIP matching is a problem for the analysis. There will be one to one matching among one party's stronghold and averaging across the others. It throwing away variation in a skewed way.

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PLoS One. 2026 Feb 3;21(2):e0342063. doi: 10.1371/journal.pone.0342063.r002

Author response to Decision Letter 1

3 Nov 2025

PONE-D-25-30422

Regional political climate's moderating role in the association between political conservatism and COVID-19 vaccine hesitancy in the United States

PLOS ONE

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PLOS ONE

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Author Response: The file has been edited to match the style requirements.

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This research was funded by a Public Affairs and Policy Research Initiative Grant from Colgate University.

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Author Response: Statement added to financial disclosure statement

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Author Response: Statements were removed from end of paper and all information is provided in Methods.

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Additional Editor Comments:

I. Major comments

Author Response: Thank you for this thoughtful suggestion and references. We have significantly edited the introduction to reflect these suggestions.

Author Response: Thanks for these constructive suggestions. We added a PLOS One reference for increased accessibility. We also created a Participant Recruitment section and included a detailed description of Qualtrics Panels. We also included the section Attention Checks under Methods and Materials with a description of additional attention checks used in our survey (as recommended by original ref 26) to ensure quality data from respondents.

In line 85, you stated that participants were recruited from USA and zip codes seem to have been important variables when going through the PROCEDURES sub-section. Could you then go into more details with the geographic description of participants' living places in line 85: specific states where the study was conducted, in urban vs rural areas, in which zip code area, while explaining what zip codes represent in USA and perhaps their corresponding variables outside USA for international readers?

Author Response: We added a table with number of participants by state and included a description of zip codes in the Procedures sub-section. We do not currently have data on the population density or size of the zip codes.

Along with the suggestion to describe Qualtrix panels when mentioning the study design, consider completing the information in the sentence "Qualtrix panel...income" (lines 87 and 88) with specification in the PARTICIPANTS sub-section of further explicit information about the inclusion and exclusion criteria which are somewhat unclear in lines 88-94? For instance, what age, sex and gender groups did you include? What political ideology? What about COVID-19 vaccination status? Did you exclude individuals with neurocognitive deficiencies given that they may affect data quality (see "Data quality of platforms and panels for online behavioral research | Behavior Research Methods" https://link.springer.com/article/10.3758/s13428-021-01694-3)?

Author Response: We added content to the Participant Recruitment section and added the Attention Check section to address these important questions.

Why did you include only two racial groups? What about participants'ethnic groups? What guided the choice of the income limit for participants? Did you include some zip codes to target particular economic groups of participants? What sampling method did you use for participants selection? How did you estimate the sample size?

Author Response: We clarified these important issues in the Study Design section, indicating that beyond the race and income quota groups, which were used for the larger project from which this data is extracted, there were no exclusions. Sample size was based on funding available for this project. Our goal was to recruit the largest number of participant possible within our budget. All participant compensation is managed through Qualtrics Panels, with the researcher paying a flat fee for the recruitment of the sample size. Given the planned analyses, we could have recruited fewer participants and still had statistical power, but hoped to maximize our funding and recruit a larger sample.

Could you add a "Data items or Variables" sub-section in between the PARTICIPANTS and PROCEDURES sub-sections to clearly describe the Qualtrix panel data used in this study with the variables (e.g., participants' sociodemographic and economic variables, political conservatism and its modalities, political climate and its modalities, and COVID-19 vaccine variables)? That description needs to be accompanied by a QUALTRIX questionnaire template used uploaded as a supplemental material.

Author Response: We created the Variables heading and added the study materials to the supplement from the online repository.

Consider reorganizing the current PROCEDURES sub-section to more clearly and deeply describe each of the research procedures carried out, except for statistical analysis which is described in a specific sub-section after the PROCEDURES sub-section: protocol preparation and registration, getting ethical matters sorted, participant enrolment (including the spread of questionnaires online, information of the target population and getting participants' responses), collection of survey responses/data, data storage before analysis (and eventually exportation towards the statistical analysis software).

Author Response: We reorganized the Procedures sub-sections to improve the clarity and readability of this section.

Could you complete the sentence in lines 96 and 97 with the IRB reference number?

Author Response: Added.

Given the inclusion of only two racial groups, can you claim to have complied with the recommendation for diversity and inclusivity when selecting study participants as mentioned in the 2024 Declaration of Helsinki? See line 97.

Author Response: Yes, this is an excellent question. The Declaration of Helsinki required justification for exclusion of participants based on demographic characteristics. The proposal for the initial study included a strong rationale, which was evaluated and approved by the Institutional Review Board. Because the goal of the study was to assess the unique influence of racial and economic marginalization, specific racial and economic groups were selected. The inclusion of White and Black participants allowed for the assessment of the unique contributions of race, income, and marginalization. These criteria were not relevant to the present analysis and certainly present a significant limitation that should be addressed in the limitations statement.

This issue should be mentioned in the limitations statement of the Discussion section when commenting on the generalizability of study findings.

Author Response: Absolutely, the statement in the limitations section reads, “Additionally, our sample was recruited through an online platform and only includes participants with access to this platform. Further, our sample includes only White and Black participants, and may not generalize beyond these demographics.”

The sentence in lines 98-100 would better be moved to the PARTICIPANTS sub-section, and the study period would better be mentioned at the beginning of the Materials and Methods section.

Author Response: The Materials and Methods section was reorganized to address these concerns.

I suggest adding a sub-section for the definition of operational terms (political conservatism, political climate, and COVID-19 vaccine terms) in between the PROCEDURES and STATISTICAL ANALYSIS sub-sections.

Author Response: We have updated to include the operational terms for these variables.

The STATISTICAL ANALYSIS sub-section also needs more precision about data curation and quality control.

Author Response: Content was added to Participant Recruitment address data curation and quality control.

Could you be more specific about the type of logistic regression performed for the main association analysed?

Author Response: We have updated the Statistical Analysis to specify regression types.

What were the independent and dependent variables? How did you select the covariates?

Author Response: Predictor and outcome variables are now identified in Study Design. Control variables were selected based on existing literature. They were included to determine the association between predictors and outcomes when controlling for known associations.

Along this line, it seems that keeping selecting race as a covariate (as did the authors) in association analyses could contribute to persistence of racial inequities in social (notably health) sciences research: see "“We adjusted for race”: now what? A systematic review of utilization and reporting of race in American Journal of Epidemiology and Epidemiology, 2020–2021 | Epidemiologic Reviews | Oxford Academic" https://academic.oup.com/epirev/article/45/1/15/7288093?login=false.

Author Response: This is an excellent point. Previous analysis of this data found race to be significantly associated with vaccine attitudes and behavior. Therefore, our goal in the present research was to explain variance in vaccine attitudes and behavior beyond that which we already know can be explained by race.

Did you perform sensitivity analyses?

Author Response: While we did not predict our findings to apply at the state level, we did run all models at the state level. While the other associations were consistent, the political climate variable was no longer significant. We did not predict that this analysis would be similar given the lack of granularity of climate at the state-level, but we could include this analysis at the editor’s discretion. In the submitted revision, we revised the vaccine and booster hesitancy scales consistent with the findings of our confirmatory factor analysis. Assessing the variables in this way did not alter the significant associations found in our predictor variables (i.e., political orientation, political climate, t

Attachment

Submitted filename: Response to Reviewers RD.docx

pone.0342063.s006.docx^{(129.2KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0342063.r003

Decision Letter 1

Mickael Essouma

25 Nov 2025

Dear Dr. Kmush,

Please submit your revised manuscript by Jan 09 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Mickael Essouma, M. D.

Academic Editor

PLOS ONE

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If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

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Additional Editor Comments:

The authors have fully addressed my comments about the methodology. However, upon checking the results and discussion, there are some issues that need to be addressed before the manuscript can be accepted for publication. Notably, the total of percentages should be 100 for each variable on Table 1. Did you exclude the Simpson paradox given the lack of effect modification of main study results by age, sex, ethnicity and so on? I suggest separating results of moderation analysis from results of main analyses in tables 3-6. Could you simplify the titles of those tables without mentioning the covariates in titles? Regarding the titles of tables 5 and 6, did you wish to say linear rather than logistic regression?

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

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

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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The PLOS Data policy

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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

Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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Reviewer #1: Dear Author(s),

This revised manuscript presents a high-quality, methodologically sound, and policy-relevant study that makes a clear contribution to the existing literature. The improvements made in response to prior feedback—especially the enhanced theoretical framing, clearer explanation of variables, and expanded discussion—demonstrate great effort and scholarly care. The paper is now well-structured, coherent, and supported by strong empirical evidence. This is a strong, well-developed manuscript. I commend the authors for their thorough revisions and recommend the paper for publication in its current form.

Sincerely,

Reviewer #3: The authors reflect each of all my review comments logically and rigorously. Therefore, I believe this manuscript is publishable to international readers. I hope this manuscript is cited and read by many readers across the globe.

Reviewer #4: Dear Editor,

Thank you for having given to me the opportunity to review the manuscript titled "The Moderating Role of Regional Political Climate on the Association Between Political Conservatism and COVID-19 Vaccine Hesitancy in the United States." This article investigates whether local political climate, measured by Republican vote share at the ZIP code level in the 2020 presidential election, moderates the association between self-reported political conservatism and COVID-19 vaccine hesitancy and uptake. The authors find that progressives exhibit low hesitancy and relatively high vaccine uptake across all contexts, while conservatives are less hesitant and more likely to receive a response in more progressive environments than in conservative ones.

The topic is highly timely and important, and the manuscript has several strengths: the research question is clearly articulated, the analytical strategy is transparent, and the psychometric properties of the hesitancy scales are robust. The provision of open data and materials, often overlooked in survey-based research, is certainly an excellent foundation.

Although the article has improved, thanks in part to previous comments, I believe its overall contribution is modest, and several conceptual and methodological issues substantially limit the conclusions that can be drawn from the results.

First, the central model, according to which conservatism and a conservative regional context are associated with greater COVID-19 vaccine hesitancy, is already well documented in the literature (I don't think it's necessary to cite previous studies published on the topic here, given their vast and therefore easily accessible nature). The manuscript's claim to novelty likely rests on two aspects. (a) The interaction between ideology and local political climate. These interaction effects are statistically significant but small, derived from self-reported cross-sectional data in a non-probability sample and estimated with a crude ecological proxy for the context. The article is not fully convinced that this adds more than an incremental refinement to what is already known. (b) Certainly more interesting, and the main contribution to the article, is to have identified how those politically aligned to the right, or at least with conservative views, tend to be less hesitant, and therefore more willing, to vaccinate if they live in more progressive areas. This is likely related to social pressure from the context, which would explain this result, which is certainly relevant to note.

Second, the operationalization of "political climate" as a Republican vote share is understandable but rather limited. This variable almost certainly combines political norms with structural and demographic characteristics such as urbanity, racial composition, socioeconomic status, and access to healthcare. No ecological controls are included in the control variables, and the analysis does not account for clustering by geographic area. This raises serious concerns about ecological confounding and underestimated standard errors, weakening any interpretation of climate as an independent contextual influence on individuals.

Third, the sampling strategy and the resulting analytic sample limit generalizability. The study uses a non-probabilistic online panel, excludes a large fraction of initial respondents, and then further restricts the sample to Black and White participants with valid zip codes. These choices are not unreasonable, but they require a much more explicit discussion of the selection processes and the limitations of demographic inference than currently provided.

Finally, some aspects of the structure and interpretation require revision. In several places, the manuscript lapses into causal and perhaps politically biased language that is not supported by cross-sectional observational data, as it should be. The suggested implications for the targeting of conservatives in progressive regions are potential and should be clearly formulated as hypotheses for future investigations rather than as certainties requiring further research. The authors rightly emphasize that the data are cross-sectional and that causal inferences cannot be drawn; they explicitly state that “we cannot assume causal or directional associations.” However, causal language still appears intermittently, for example in phrases such as “the impact of liberal political climates on conservative individuals has significant implications” in the abstract. I would recommend revising the manuscript to replace such language with more neutral formulations (“association,” “pattern,” “relationship”) and to avoid implying that climates influence or exert effects on individuals in the absence of longitudinal or quasi-experimental evidence.

This is not to suggest that this is a fatal flaw that absolutely precludes publication. The article is well-argued, follows academic standards, and offers new, interesting, and partly original positions and arguments, as an article should. As mentioned, I would recommend a light revision, with particular attention to clarifying the modest incremental contribution and broadening and deepening the discussion of limitations.

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PLoS One. 2026 Feb 3;21(2):e0342063. doi: 10.1371/journal.pone.0342063.r004

Author response to Decision Letter 2

8 Jan 2026

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PLoS One. doi: 10.1371/journal.pone.0342063.r005

Decision Letter 2

Mickael Essouma

18 Jan 2026

Regional political climate's moderating role in the association between political conservatism and COVID-19 vaccine hesitancy in the United States

PONE-D-25-30422R2

Dear Dr. Kmush,

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Additional Editor Comments (optional):

there is a typo in line 120.

In tables 3 and 4, consider replacing B with β.

In tables 5 and 6 and in the title of table 6, «vaccine hesitancy» should be replaced with «vaccine status», and «booster hesitancy» should be replaced with «booster status».

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0342063.r006

Acceptance letter

Mickael Essouma

PONE-D-25-30422R2

PLOS One

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Associated Data

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

Supplementary Materials

S1 File. Items from Life Experiences and COVID-19 – Qualtrics.

(DOCX)

pone.0342063.s001.docx^{(21.1KB, docx)}

S2 File. Confirmatory Factor Analysis.

(DOCX)

pone.0342063.s002.docx^{(15.9KB, docx)}

S3 Table. Participant Distribution by State.

(DOCX)

pone.0342063.s003.docx^{(18.6KB, docx)}

S4 File. Results for All Regression Models with Control Variables.

(DOCX)

pone.0342063.s004.docx^{(29.9KB, docx)}

Attachment

Submitted filename: Response to Reviewers RD.docx

pone.0342063.s006.docx^{(129.2KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0342063.s007.docx^{(19.6KB, docx)}

Data Availability Statement

The materials and data presented in this study are openly available in OSF at https://osf.io/s2knw/?view_only=4de7d7296d0a44acab11174e357776b.

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PERMALINK

Regional political climate’s moderating role in the association between political conservatism and COVID-19 vaccine hesitancy in the United States

Rachel E Dinero

William B Monti

Brittany L Kmush

Roles

Abstract

Introduction

Materials and methods

Study design

Predictor variables

Outcome variables

Participant recruitment

Research ethics

Statistical analyses

Results

Participant characteristics

Table 1. Participant age, gender, race, vaccine status, booster status, income, and education, United States, 2023 (N = 683).

Fig 1. Frequency distributions for vaccine hesitancy, booster hesitancy, political conservatism, and % of Republican vote lead in the 2020 presidential election based on participant zip code (N = 683).

Individual associations between predictors and outcomes

Regression analysis

Table 3. Linear regression testing for main effects of political conservatism and conservative regional climate on vaccine hesitancy and booster hesitancy, United States, 2023.

Table 4. Linear regression testing for main effects of and the interaction between political conservatism and conservative regional climate on vaccine hesitancy and booster hesitancy, United States, 2023.

Table 5. Logistic regression for main effects of political conservatism and conservative regional climate on vaccine status and booster status, United States, 2023.

Table 6. Logistic regression testing for main effects of and the interaction between political conservatism and conservative regional climate on vaccine stauts and booster status, United States, 2023.

Discussion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Mickael Essouma

Roles

Author response to Decision Letter 1

Decision Letter 1

Mickael Essouma

Roles

Author response to Decision Letter 2

Decision Letter 2

Mickael Essouma

Roles

Acceptance letter

Mickael Essouma

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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