The Vaccine-Education Paradox in a Cross-Country Analysis: Education Predicts Higher and Lower Vaccination Rates

Abstract

Research has shown a mixed relationship between education and vaccination rates. In the current analysis, we aimed to determine the relationship between educational level and coronavirus disease 2019 (COVID-19) vaccination rates. We performed a cross-country analysis on data from 133 countries. Correlation analyses showed that higher and better education was associated with higher COVID-19 vaccination rates. When we performed the regression analysis including the education, health system, and economic development variables, education-COVID-19 vaccination relationships were mostly reversed. In particular, in wealthy countries, as the mean years of schooling decreased and the pupil-teacher ratio increased, COVID-19 vaccination rates increased. In less affluent countries, with greater education expenditures, COVID-19 vaccination rates decreased. We explain these contradictions by describing links between vaccination rates, life expectancy, and education-related variables. Our findings may aid in promoting more effective uptake of COVID-19 vaccination.

Plain Language Summary

In this study, we examined the association of education level with vaccinations against COVID-19- using existing data from 133 countries. We found out that in countries with better and longer education, more people are vaccinated against COVID-19. We also found that in poor countries, where the government spends less on education, less people were vaccinated. In wealthy countries where the education was of shorter duration and worse quality, more people were vaccinated. Our study showed surprising results - better education might not lead to more vaccinations. We believe that the cause of this lies within a more individualist approach that is developed with better education. The study findings can be used to promote vaccinations, especially among the well-educated population.

Keywords

COVID-19 education vaccination human development index

Introduction

Mass coronavirus disease 2019 (COVID-19) vaccination has been an effective measure for mitigating the global COVID-19 pandemic (Viana et al., 2021). Thus, governments have allocated considerable resources to develop vaccines and implement vaccination programs to fight the pandemic and its effects. Vaccines developed against COVID-19 effectively reduce COVID-19 symptoms (Moghadas et al., 2021) and are particularly effective at reducing the spread of the pandemic when more than 67% of the population is vaccinated, reaching a state of herd immunity (Randolph & Barreiro, 2020). However, in numerous countries, vaccination programs have been less effective because many people do not want to be vaccinated against COVID-19, making it challenging to reach herd immunity. There is indeed substantial variation in COVID-19 vaccination rates between countries, ranging from 2.13 to 98.99 people per hundred in Yemen and the United Arab Emirates, respectively (Mathieu et al., 2020, 2021). Thus, identifying the determinants of these differences is critical.

A large body of research has identified factors influencing vaccine acceptance or avoidance (Larson, 2018; MacDonald, 2015). However, little is known regarding the socioeconomic factors determining vaccination rates at the country level. Researchers indicate that such determinants might be responsible, to a large extent, for differences in vaccination acceptance rates. According to Ning et al. (2022), the significant differences in vaccination progress across countries stem from the national economies, health systems, and educational system developmental levels. Even before the pandemic, various health, educational, and socioeconomic determinants of vaccination acceptance and hesitancy have been identified. These range from individual-level to country-level factors (Moran et al., 2017). Similar studies have indicated numerous such determinants during the pandemic. Basak et al. (2022) demonstrated that the wealthier the country, the higher the COVID-19 vaccination rate. The level of economic growth of a country (expressed as the gross national income [GNI]) is a component of the Human Development Index (HDI), a summary measure of a country’s average development.

HDI also includes indicators of health (life expectancy at birth [20–85 years]) and educational development (mean [0–15] and expected [0–18] years of schooling). Countries with higher HDI scores have higher COVID-19 vaccination rates than those with lower HDI scores (Ning et al., 2022). There are many compelling reasons for greater wealth and better health care quality translating into increased vaccination rates. These explanations relate mainly to the level of financial investment in vaccine development, production, and promotion, as well as the quality of health resources and infrastructure (Basak et al., 2022; Ning et al., 2022).

The relationship between education and vaccination appears to be more complex and heterogeneous than that between economic or health resources and vaccination. Most studies indicate that higher education is associated with higher vaccination acceptance (Czajka et al., 2020; Raghupathi & Raghupathi, 2020), including COVID-19 vaccination (Hacquin et al., 2020; Kricorian et al., 2022). However, some studies report contrary findings, specifically, that less-educated individuals are more likely to vaccinate their children (Feiring et al., 2015; Grandahl et al., 2017) or themselves (Opstelten et al., 2001; Tran et al., 2018) against COVID-19 (Bhartiya et al., 2021; Salali & Uysal, 2020) than more-educated individuals. To illustrate the ambiguity of the vaccination-education relationship, Lazarus et al. (2020) analyzed data from 19 countries and reported that, in general, higher levels of education are positively associated with COVID-19 vaccination acceptance.

Better-educated people are reported to have higher health and vaccine literacy levels, as well as a greater understanding of the need for widespread vaccination (Biasio et al., 2020; Vikram et al., 2012). Moreover, they are less likely to accept misinformation (Scherer et al., 2021). As Sharon and Baram-Tsabari (2020) claim, highly educated people most likely depend on reliable sources of information and tend to search for expert opinions rather than accept misinformation easily; they are usually more analytical than the less educated. In contrast, better-educated people aiming to enrich their knowledge by cross-referencing reputable information on the Internet may inadvertently gather incorrect information about vaccination (Kata, 2010). They might also forgo vaccination (own or children’s) due to the individualist epistemology resulting from their better education (Ten Kate et al., 2021).

While the existing literature identifies economic and health variables significantly associated with vaccination acceptance or hesitancy, the effects of the educational variables on vaccinations are mixed and complex. In the current analysis, by means of cross-country comparative analyses, we address the challenges of these complex relationships by: (1) analyzing data from a large number of countries; (2) controlling for economic and health variables at the country level; and (3) analyzing the actual COVID-19 vaccination rates rather than relying on subjective data of attitudes toward vaccinations. To strengthen public health policies, a better understanding of the relationships between educational factors and vaccination rates is critical for current and future pandemics or epidemics. Thus, this study aimed to establish the association between educational factors and COVID-19 vaccination rates. We explored the question: What is the relationship between education and COVID-19 vaccination? Our findings may help in developing targeted health education and promotion initiatives to improve vaccine uptake.

Materials and Methods

Two datasets were used in a cross-national comparison of aggregated data to examine the relation of specific components of the HDI (mean years of schooling, expected years of schooling, life expectancy at birth, and GNI per capita) with vaccination rates. The components are described in detail in Supplemental Table 1. The datasets also consisted of information on variables that we controlled for, including pupil-teacher ratio and expenditures on education and health (percentage of gross domestic product [GDP] dedicated to education or health). The controlled variables alongside the HDI and its components were collected from the Human Development Reports 2020 (United Nations Development Programme, 2020). We treated mean and expected years of education as indicators of education duration, and education expenditure and pupil-teacher ratio as indicators of education quality.

Table 1.

Hierarchical Linear Regression with Vaccination Rate as Dependent Variable.

Independent variables	Model 1A	Model 2A	Model 3A	Model 1B	Model 2B	Model 3B
Human development index	0.746***	0.772***	0.811***	—	—	—
Life expectancy at birth	—	—	—	0.647***	0.627***	0.613***
Mean years of schooling	—	—	—	−0.252*	−0.247*	−0.258*
Expected years of schooling	—	—	—	0.332**	0.377**	0.374**
GNI per capita	—	—	—	0.069	0.085	0.085
Health expenditure	—	−0.033	−0.034	—	−0.058	−0.057
Education expenditure	—	−0.059	−0.067	—	−0.063	−0.065
Pupil-teacher ratio	—	—	.046	—	—	−0.032
R ²	.557	.563	.563	.633	.642	.642
ΔR²	—	.005	.000	—	.008	.000
F	164.9***	55.4***	41.3***	55.3***	37.6***	32.0***

Note:*p < .05; ^**p < .01; ^***p < .001.

The selection of these educational indicators was guided by global data availability. We wanted to ensure that these indicators were differentiated to reflect the different aspects of education. We controlled for overall health expenditure, as it might lead to greater vaccination rates by influencing health systems and because it is the health equivalent of education expenditure. Furthermore, since the effect of education on vaccination may vary by country wealth (Larson et al., 2016; Loke et al., 2017), we conducted separate analyses for more and less affluent countries. We believe this to be reasonable since most studies reporting aversion to vaccination among highly educated people were conducted in affluent countries (Bhartiya et al., 2021; Feiring et al., 2015; Grandahl et al., 2017; Opstelten et al., 2001; Salali & Uysal, 2020; Tran et al., 2018).

Data on vaccination rates were collected from the “OurWorldInData” COVID-19 vaccination global database (Mathieu et al., 2020, 2021). Missing vaccination rates on the day the data were downloaded (April 10, 2022) were filled with data from the closest complete report. Thus, we compiled a dataset of 144 countries with seemingly complete data. Finally, the number of countries included in the analyses equaled 133 because of missing data on education expenditure, health expenditure, or pupil-teacher ratio in Hong Kong, Montenegro, Libya, Turkmenistan, Bosnia and Herzegovina, Iraq, Bolivia, Venezuela, Palestine, and Nigeria. The sample comprised more than 90% of the total human population in 2020. Supplemental Table 2 reports the data for each country.

Table 2.

Linear Regression with Vaccination Rate as Dependent Variable for High and Middle GNI.

Independent variables	High GNI (n = 77)	Middle GNI (n = 66)
Human development index	—	—
Life expectancy at birth	0.579**	0.413**
Mean years of schooling	−0.393***	−0.104
Expected years of schooling	0.222	0.284
GNI per capita	0.266*	0.072
Health expenditure	−0.084	0.002
Education expenditure	0.156	−0.253*
Pupil-teacher ratio	0.189*	−0.118
R ²	.609	.511
ΔR²	—	—
F	14.25***	7.92***

Note:*p < .05; ^**p < .01; ^***p < .001.

No ethical approval was needed for the current analysis, as according to Adam Mickiewicz University institutional guidelines, it is only needed for studies with living subjects.

The data were analyzed using hierarchical linear regression and correlation analyses performed in JAMOVI (The jamovi project, 2021). The data were visualized in R 4.1.2 (R Core Team, 2021) using libraries ggplot2 (Wickham, 2016), sf (Pebesma, 2018), rnaturalearth (South, 2018), rnaturalearthdata (South, 2017), and ggcorrplot (Kassambara, 2019).

Results

As shown in Figure 1 and Supplemental Table 2, the lowest vaccination rates were in Africa and the Middle East, particularly in Yemen (2.13). In the Middle East, vaccination rates were the highest in the United Arab Emirates (98.99). The HDI and its components had similar distributions. The highest percentage of GDP per capita was dedicated to education expenditures in Botswana (11%) and Yemen (10%), closely followed by the Nordic countries, while the lowest was in China, Myanmar, and Bangladesh (2%). Bangladesh also had the lowest health expenditure (1.2%), while the USA had the highest (17.1%). The pupil-teacher ratio was the highest in Africa and the Middle East (e.g., Rwanda (60:1) but lowest in Luxembourg (8:1).

Figure 1.

Maps representing components of HDI, Vaccination rate, pupil-teacher ratio, education expenditure, and health expenditure across the 133 countries included within the study.

In Figure 2, the correlation analysis indicated many significant positive relationships between variables, except for the pupil-teacher ratio, which was significant but negative. Non-significant correlations occurred with (1) vaccination rate-education expenditure; and (2) life expectancy-education expenditure.

Figure 2.

Spearman’s rho correlations between the variables used in the analysis.

Tables 1 and 2 show the hierarchical linear regression analysis results. In Model A, we tested for the influence of HDI on vaccination rates. The regression results were significant at all stages, explaining 56% of the variance at the last stage (F(5, 128) = 32.8, p < .001), although HDI was the only significant predictor of vaccination rates (β = .794, p < .001). We have also tested how this model changes with HDI components being separately introduced into the model (Model B). The model was significant (F(7,126) = 32.0, p < .001) and explained a greater deal of variance (64 %), although at the final stage, only life expectancy (β = .585, p < .001), mean years of schooling (β = −.297, p = .012), and expected years of schooling (β = .367, p = .002) were significant.

Next, based on prior studies, we tested two additional models by categorizing GNI according to the World Bank (Hamadeh et al., 2021) into high (>$12,535), middle-high ($4,046–$12,535), middle-low ($1,035–$4,045), and low (<$1,035). Only one of the analyzed countries (Malawi) was at the bottom of the GNI spectrum with a GNI value of $1,034.67. Due to the closeness of this value to the lower limit of the middle-low GNI category, Malawi was included in this group. Furthermore, middle-low and middle-high GNI categories were merged into a middle GNI group due to low group sizes (n = 38; n = 28) in these two subgroups. GNI was also included as a predictor within those models because the values of variables within the groups differed greatly. For instance, high GNI values ranged from $88,155.21 in Singapore to $12,707.36 in Sri Lanka. For the middle GNI group, the regression was significant (F(7, 59) = 7.92, p < .001)—life expectancy (β = .413, p = .004) and education expenditure (β = −.253, p = .019) were significant predictors of the vaccination rate. The high GNI model was also significant (F(7, 70) = 14.25, p < .001) and had four significant predictors: life expectancy (β = .579, p = .001), mean years of schooling (β = −.393, p < .001), GNI (β = .266, p = .024), and pupil-teacher ratio (β = .189, p = .038).

Discussion

Previous studies have shown mixed results of the education-vaccination relationship, which we have attempted to clarify in this cross-national analysis. Our correlation analyses indicated that the three indicators of education we used in this study that is, mean and expected number of years of schooling and pupil-teacher ratio show that the better the education in a country, the greater the number of people vaccinated against COVID-19. However, the regression model, including educational, health, and economic indicators, showed that the higher the quality and quantity of education, the lower the COVID-19 vaccination rate. This result is because, as (1) the mean number of years of education increased in wealthier countries, (2) the teacher-pupil ratio increased in wealthier countries, and (3) education expenditure increased in poorer countries, the vaccination rate decreased. The expected years of education were associated with higher vaccination rates in the models without GNI categorization.

The correlations between educational variables and vaccination ranged between .59 and .71, showing that various indicators of education (mean and expected number of years of education, pupil-teacher ratio) were associated with the COVID-19 vaccination rate. This result suggests that the higher and better the education level in a given population, the higher the vaccination rates against COVID-19. These results are consistent with previous findings, indicating that better-educated individuals are more likely to be vaccinated (Czajka et al., 2020; Raghupathi & Raghupathi, 2020), including against COVID-19 (Hacquin et al., 2020; Kricorian et al., 2022). Among the five education indicators included in this analysis, only current education expenditure was unrelated to COVID-19 vaccination. The lack of significance of this relationship may be explained by the fact that current expenditure on education may, to a lesser extent, reflect the quality of adult education (which took place in the past, even a few decades earlier) and who makes decisions about vaccination. It also reflects the current quality of education of children, who are unable to make the decision to vaccinate by themselves.

It is crucial to determine the relationship between education and vaccination and control for the influence of latent variables (country wealth and healthcare quality) that may be related to the former. When the educational, health, and economic variables were introduced into the regression model, the relationship between education and vaccination levels became a combination of both positive and negative effects. Mean years of schooling was a negative predictor of vaccination rates in analyses including either all 133 countries or wealthy countries (with high GNI) only, whereas the pupil-teacher ratio was also a significant positive predictor. This means that the greater the average number of pupils per teacher in a country, the more likely it is for people to be vaccinated. Thus, fewer pupils per teacher were associated with lower vaccination rates, indicating that the higher the quality of education, the greater the vaccination hesitancy rate. In poorer countries (with middle GNI), educational expenditure negatively predicted COVID-19 vaccination rates. Previous studies support such results indicating that higher educational level may be associated with lower rates of the willingness for own (Opstelten et al., 2001; Tran et al., 2018) and ones’ children’s vaccination (Ten Kate et al., 2021), especially in wealthy countries.

In our opinion, the relationship between life expectancy at birth and education may be vital to understanding these surprising regression analysis results. We found that life expectancy at birth, an indicator of the quality of the healthcare system (Jaba et al., 2014; Olshansky et al., 2005), strongly predicted COVID-19 vaccination rates at the country level. Furthermore, as shown in Tables 1 and 2, various educational level indicators had a positive and robust relationship with life expectancy at birth. Earlier studies explained the relationship between education and health in various ways. For example, better-educated individuals are more often engaged in healthy behaviors, including healthy eating and non-smoking (Margolis, 2013). Since we included life expectancy in the regression model when testing for the relationship between the educational indicators and COVID-19 vaccination rates, we also controlled for the extent of the shared variance between educational indicators and life expectancy. Therefore, we concluded that when controlling for better-educated people’s characteristics (such as healthy behaviors), we can better understand their longer life expectancy. The mostly negative relationship (although positive for the pupil-teacher ratio), between the three educational indicators (mean years of schooling, education expenditure, and pupil-teacher ratio) and COVID-19 vaccination rates suggests that when the relationships between education and life expectancy are eliminated (e.g., by controlling for healthy behaviors), the more-educated population would be less likely to vaccinate against COVID-19.

Therefore, we suggest that when analyzing the negative relationships between education and vaccination, health behavioral issues and other factors that lead better-educated individuals to live longer should be eliminated; rather, other characteristics of better-educated people that do not contribute to the improvement of their health status (increase in life expectancy and mortality), but which contribute to anti-vaccination attitudes, should be taken into account. Some of the characteristics of highly educated individuals that may explain anti-vaccine attitudes that are unlikely to be associated with longer life expectancy were reported by Ten Kate et al. (2021). The authors conducted in-depth interviews with highly educated parents who did not want to vaccinate their children in the Netherlands, a wealthy country on the higher end of the GNI spectrum. The authors showed that these parents hold individualist epistemology, which is believed to play a key role in how an individual obtains knowledge and searches for truth. Ten Kate et al. (2021) found that individualist epistemology leads to vaccine skepticism in two ways: (1) a neo-romantic one, in which people believe that they will find the truth based on their feelings and intuition, which guides them to natural solutions regarding their health dilemmas; and (2) a critical-reflexive one, in which people search for truth using scientific methods that allow them to challenge the current scientific consensus. These findings are supported by those of previous studies demonstrating that strong individualistic worldviews are positively associated with anti-vaccination attitudes (Hornsey et al., 2018). More research is needed to explain why people with more years of education are less likely to be vaccinated, but the individualistic approach may explain our main findings.

Furthermore, to explain our results, we highlight the differences between countries’ mean and expected years of schooling (see Figure 1). For example, for the mean years of schooling, there are large gaps between Western countries (Europe and Australia) and the rest of the world. In contrast, for the expected years of schooling, the differences between Western countries and Asian, Latin, and South American countries seem smaller. These differences between the two aspects of education duration are because the mean years of education imply the current educational level of the population in a given country, whereas expected years of schooling indicate the future schooling level (Smits & Permanyer, 2019). Thus, in rich (Western) countries, the mean years of schooling may result in adults developing an individualistic approach, leading to an increase in anti-vaccine attitudes (Ten Kate et al., 2021). In contrast, the expected years of education do not reflect the development of individualistic attitudes. Discrepancies in the relationship between vaccination rate and mean and expected years of schooling suggest that the younger generation of well-educated individuals may be better able to judge the credibility of health information sources and show more pro-vaccination attitudes than older individuals with higher levels of education; this aspect should be explored in future studies.

On another note, our study shows that HDI, although a great predictor of vaccination rates, is less effective than its components. As reported in the regression analysis, the model with HDI explained less variance in COVID-19 vaccination (56%) than the model in which HDI was divided into its components (64%). This is partially because we found intricacies in education-related HDI components, that is, the positive effect of expected years of schooling and the negative effect of mean years of schooling on vaccination rates. Therefore, we encourage testing separate components of the HDI index instead of the entire index in future vaccination rate studies.

Practical Implications

The results of our study may be applicable to planning for targeted vaccine promotion. We found that in wealthy countries, individuals who require special encouragement to be vaccinated against COVID-19 are highly educated. This opposes the most commonly targeted health campaign recipients, the less educated and poor (Koelen & Van den Ban, 2004). Specific strategies to promote COVID-19 vaccination in highly educated individuals from wealthy countries should be developed. Future research should develop effective persuasive measures to promote the uptake of vaccination in this population. Practitioners seeking to increase vaccine uptake among highly educated individuals could also consider their individualistic attitudes toward vaccination decisions. Better-educated individuals may rely less on medical authorities to make vaccination decisions and more on their ability to find and evaluate scientific sources that describe the benefits and risks of vaccination. For instance, as Kata (2010) indicated, they might search for medical information on the Internet and discover numerous misinformation that they may not always interpret correctly. Individualistic attitudes are associated with reactance (Jonas et al., 2009)—resistance feelings against external freedom restriction through overt persuasion. In turn, reactance is associated with a negative attitude toward COVID-19 vaccination (Drążkowski & Trepanowski, 2021). Therefore, encouraging highly educated people to vaccinate should be subtle so as to not induce states of reactance in them, by using various forms of self-persuasion (Drążkowski et al., 2022).

Limitations

Although our study provided a new perspective on the vaccination issue, it has some limitations. First, although our total sample size appears large (about 90% of the total human population aggregated into 133 countries), it did not include countries from the lower end of the GNI spectrum as data for those countries were incomplete. To obtain more representative findings, these countries should be included in further studies when such data become available. The inclusion of a greater number of countries in the analyses should also allow a safe increase in the number of analyzed variables. This is related to a possible future direction for studies in this area—the exploration of our hypothesis that individualist epistemology affects vaccination rates. This could be explored at an individual level as well as at a country level with data on cultural dimensions or values. Next, although there were no significant differences in datasets concerning the data collection time, each used a different methodology, which might have influenced the results. Finally, we did not use any data on different education types. For instance, people educated in the social sciences might have a different perception of vaccination than those in medicine. Thus differences in the willingness to vaccinate might exist between such groups.

Conclusions

In a cross-national comparison of aggregated data from 133 countries, this study indicated a paradoxical relationship between education and COVID-19 vaccination rates. On the one hand, correlational analysis results showed that higher and better education is associated with higher COVID-19 vaccination rates. On the other hand, when the used set of educational, health, and economic variables were entered into the regression analysis, most of the education-COVID-19 vaccination relationships were reversed, and better education led to lower vaccination rates, especially in rich countries. This difference between the correlation and regression analyses indicates the potentially spurious nature of the positive relationships between education and COVID-19 vaccination observed in previous studies with individual-level analyses (Bhartiya et al., 2021; Salali & Uysal, 2020). Nevertheless, we explain these contradictions by the fact that the main predictor of vaccination rate in regression analyses is life expectancy, which in turn is the result of better medical care as well as education. Thus, when controlling for the relationship between education and health, higher education unrelated to health and long life was associated with lower vaccination rates. What higher education brings about, unrelated to health but related to anti-vaccination attitudes, is the individualistic attitude that characterizes some well-educated people from rich countries (Ten Kate et al., 2021) and is positively associated with anti-vaccination attitudes (Hornsey et al., 2018). This suggests that future research on the relationship between education and vaccination rate conducted at the individual level should consider, among the determinants of vaccination, individual differences in health-related knowledge/beliefs and individualistic attitudes.

Our findings may not clarify the relationship between education and vaccination completely. However, their careful interpretation may help foster a more profound understanding of the complex mechanisms of this relationship. Our results provide new insights informing future practices and policies that enhance vaccine uptake against COVID-19. For instance, health campaigns could encourage highly educated populations to vaccinate against COVID-19 by adjusting the campaign content to their characteristics, that is, individualist epistemology. Much remains unknown regarding the relationship between education and vaccination; therefore, we believe that future studies should continue to explore this area to maximize potential vaccine uptake, not only against COVID-19, but also against other diseases.

Supplemental Material

sj-docx-1-sgo-10.1177_21582440241253326 – Supplemental material for The Vaccine-Education Paradox in a Cross-Country Analysis: Education Predicts Higher and Lower Vaccination Rates

Supplemental material, sj-docx-1-sgo-10.1177_21582440241253326 for The Vaccine-Education Paradox in a Cross-Country Analysis: Education Predicts Higher and Lower Vaccination Rates by Radosław Trepanowski and Dariusz Drążkowski in SAGE Open

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethics Statement

No approval needed.

ORCID iDs

Radosław Trepanowski

Dariusz Drążkowski

Data Availability Statement

This study utilized existing datasets, the sources of which are referenced in the ‘Materials and Methods’ section.

Supplemental Material

Supplemental material for this article is available online.

References

Basak

Abir

Al Mamun

Zainol

N. R.

Khanam

Haque

Md. R.

Milton

A. H.

Agho

K. E.

(2022). A global study on the correlates of Gross Domestic Product (GDP) and COVID-19 vaccine distribution. Vaccines, 10(2), 1–13. https://doi.org/10.3390/vaccines10020266

Bhartiya

Kumar

Singh

Murugan

Rajavel

Wadhwani

(2021). Knowledge, attitude and practice towards COVID-19 vaccination acceptance in West India. International Journal of Community Medicine and Public Health, 8(3), 1170–1176. https://doi.org/10.18203/2394-6040.ijcmph20210481

Biasio

L. R.

Giambi

Fadda

Lorini

Bonaccorsi

D’Ancona

(2020). Validation of an Italian tool to assess vaccine literacy in adulthood vaccination: A pilot study. Annali di igiene Medicina Preventiva e di Comunità, 32(3), 205–222. https://doi.org/10.7416/ai.2020.2344

Czajka

Biłas

Pałka

Jędrusik

Czapkiewicz

(2020). Who or what influences the individuals’ decision-making process regarding vaccinations? International Journal of Environmental Research and Public Health, 17(12), 1–16. https://doi.org/10.3390/ijerph17124461

Drążkowski

Trepanowski

(2021). Reactance and perceived disease severity as determinants of COVID-19 vaccination intention: An application of the theory of planned behavior. Psychology, Health & Medicine, 27(10), 2171–2178. https://doi.org/10.1080/13548506.2021.2014060

Drążkowski

Trepanowski

Fointiat

(2022). Vaccinating to protect others: The role of self-persuasion and empathy among young adults. Vaccines, 10(4), 1–17. https://doi.org/10.3390/vaccines10040553

Feiring

Laake

Molden

Cappelen

Håberg

S. E.

Magnus

Steingrímsdóttir

Ó. A.

Strand

B. H.

Stålcrantz

Trogstad

(2015). Do parental education and income matter? A nationwide register-based study on HPV vaccine uptake in the school-based immunisation programme in Norway. BMJ Open, 5(5), 1–10. http://doi.org/10.1136/bmjopen-2014-006422

Grandahl

Tydén

Westerling

Nevéus

Rosenblad

Hedin

Oscarsson

(2017). To consent or decline HPV vaccination: A pilot study at the start of the national school-based vaccination program in Sweden. Journal of School Health, 87(1), 62–70. https://doi.org/10.1111/josh.12470

Hacquin

A. S.

Altay

de Araujo

Chevallier

Mercier

(2020). Sharp rise in vaccine hesitancy in a large and representative sample of the French population: Reasons for vaccine hesitancy. PsyArXiv. https://doi.org/10.31234/osf.io/r8h6z

10.

Hamadeh

von Rompaey

Matreau

(2021). New World Bank country classifications by income level: 2021-2022. World Bank Blogs. https://blogs.worldbank.org/opendata/new-world-bank-country-classifications-income-level-2021-2022

11.

Hornsey

M. J.

Harris

E. A.

Fielding

K. S.

(2018). The psychological roots of anti-vaccination attitudes: A 24-nation investigation. Health Psychology, 37(4), 307. https://doi.org/10.1037/hea0000586

12.

Jaba

Balan

C. B.

Robu

I. B.

(2014). The relationship between life expectancy at birth and health expenditures estimated by a cross-country and time-series analysis. Procedia Economics and Finance, 15, 108–114. https://doi.org/10.1016/S2212-5671(14)00454-7

13.

Jonas

Graupmann

Kayser

D. N.

Zanna

Traut-Mattausch

Frey

(2009). Culture, self, and the emergence of reactance: Is there a “universal” freedom? Journal of Experimental Social Psychology, 45(5), 1068–1080. https://doi.org/10.1016/j.jesp.2009.06.005

14.

Kassambara

(2019). ggcorrplot: Visualization of a correlation matrix using ‘ggplot2’ [software]. https://cran.r-project.org

15.

Kata

(2010). A postmodern Pandora’s box: Anti-vaccination misinformation on the Internet. Vaccine, 28(7), 1709–1716. https://doi.org/10.1016/j.vaccine.2009.12.022

16.

Koelen

M. A.

Van den Ban

A. W.

(2004). Health education and health promotion. Wageningen Academic Publishers.

17.

Kricorian

Civen

Equils

(2022). COVID-19 vaccine hesitancy: Misinformation and perceptions of vaccine safety. Human Vaccines & Immunotherapeutics, 18(1), 1–8. https://doi.org/10.1080/21645515.2021.1950504

18.

Larson

H. J.

(2018). The biggest pandemic risk? Viral misinformation. Nature, 562(7726), 309–310. https://doi.org/10.1038/d41586-018-07034-4

19.

Larson

H. J.

de Figueiredo

Xiahong

Schulz

W. S.

Verger

Johnston

I. G.

Cook

A. R.

Jones

N. S.

(2016). The state of vaccine confidence 2016: Global insights through a 67-country survey. EBioMedicine, 12, 295–301. https://doi.org/10.1016/j.ebiom.2016.08.042

20.

Lazarus

J. V.

Ratzan

Palayew

Billari

F. C.

Binagwaho

Kimball

Larson

H. J.

Melegaro

Rabin

White

T. M.

El-Mohandes

(2020). COVID-SCORE: A global survey to assess public perceptions of government responses to COVID-19 (COVID-SCORE-10). PLoS One, 15(10), e0240011. https://doi.org/10.1371/journal.pone.0240011

21.

Loke

A. Y.

Chan

A. C.

Wong

Y. T.

(2017). Facilitators and barriers to the acceptance of human papillomavirus (HPV)vaccination among adolescent girls: A comparison between mothers and their adolescent daughters in Hong Kong. BMC Research Notes, 10(1), 1–3. https://doi.org/10.1186/s13104-017-2734-2

22.

MacDonald

N. E.

(2015). Vaccine hesitancy: Definition, scope and determinants. Vaccine, 33(34), 4161–4164. https://doi.org/10.1016/j.vaccine.2015.04.036

23.

Margolis

(2013). Educational differences in healthy behavior changes and adherence among middle-aged Americans. Journal of Health and Social Behavior, 54(3), 353–368. https://doi.org/10.1177/0022146513489312

24.

Mathieu

Ritchie

Ortiz-Ospina

Roser

Hasell

Appel

Giattino

Rodés-Guirao

(2021). A global database of COVID-19 vaccinations. Nature Human Behaviour, 5(7), 947–953. https://doi.org/10.1038/s41562-021-01122-8

25.

Mathieu

Ritchie

Rodés-Guirao

Appel

Gavrilov

Giattino

Hasell

Macdonald

Dattani

Beltekian

Ortiz-Ospina

Roser

(2020). Coronavirus pandemic (COVID-19). OurWorldInData.org. https://ourworldindata.org/coronavirus

26.

Moghadas

S. M.

Vilches

T. N.

Zhang

Wells

C. R.

Shoukat

Singer

B.H.

Meyers

L. A.

Neuzil

K. M.

Langley

J. M.

Fitzpatrick

M. C.

Galvani

A. P.

(2021). The impact of vaccination on coronavirus disease 2019 (COVID-19) outbreaks in the United States. Clinical Infectious Diseases, 73(12), 2257–2264. https://doi.org/10.1093/cid/ciab079

27.

Moran

M. B.

Chatterjee

J. S.

Frank

L. B.

Murphy

S. T.

Zhao

Chen

Ball-Rokeach

(2017). Individual, cultural and structural predictors of vaccine safety confidence and influenza vaccination among Hispanic female subgroups. Journal of Immigrant and Minority Health, 19(4), 790–800. https://doi.org/10.1007/s10903-016-0428-9

28.

Ning

Wang

Chen

Pei

Gao

(2022). The COVID-19 vaccination and vaccine inequity worldwide: An empirical study based on global data. International Journal of Environmental Research and Public Health, 19(9), 1–13. https://doi.org/10.3390/ijerph19095267

29.

Olshansky

S. J.

Passaro

D. J.

Hershow

R. C.

Layden

Carnes

B. A.

Brody

Hayflick

Butler

R. N.

Allison

D. B.

Ludwig

D. S.

(2005). A potential decline in life expectancy in the United States in the 21st century. New England Journal of Medicine, 352(11), 1138–1145. https://doi.org/10.1056/NEJMsr043743

30.

Opstelten

Hak

Verheij

T. J.

van Essen

G. A.

(2001). Introducing a pneumococcal vaccine to an existing influenza immunization program: Vaccination rates and predictors of noncompliance. The American Journal of Medicine, 111(6), 474–479. https://doi.org/10.1016/S0002-9343(01)00880-4

31.

Pebesma

(2018). Simple features for R: Standardized support for spatial vector data [software]. The R Journal, 10(1), 439–446. https://doi.org/10.32614/RJ-2018-009

32.

R Core Team. (2021). R: A language and environment for statistical computing [software]. R Foundation for Statistical Computing. https://www.R-project.org/

33.

Raghupathi

(2020). The influence of education on health: An empirical assessment of OECD countries for the period 1995–2015. Archives of Public Health, 78(1), 1–18. https://doi.org/10.1186/s13690-020-00402-5

34.

Randolph

H. E.

Barreiro

L. B.

(2020). Herd immunity: Understanding COVID-19. Immunity, 52(5), 737–741. https://doi.org/10.1016/j.immuni.2020.04.012

35.

Salali

G. D.

Uysal

M. S.

(2020). COVID-19 vaccine hesitancy is associated with beliefs on the origin of the novel coronavirus in the UK and Turkey. Psychological Medicine, 52(15), 3750–3752. https://doi.org/10.1017/S0033291720004067

36.

Scherer

L. D.

McPhetres

Pennycook

Kempe

Allen

L. A.

Knoepke

C. E.

Tate

C. E.

Matlock

D. D.

(2021). Who is susceptible to online health misinformation? A test of four psychosocial hypotheses. Health Psychology, 40(4), 274–284. https://doi.org/10.1037/hea0000978

37.

Sharon

A. J.

Baram-Tsabari

(2020). Can science literacy help individuals identify misinformation in everyday life? Science Education, 104(5), 873–894. https://doi.org/10.1002/sce.21581

38.

Smits

Permanyer

(2019). The subnational human development database. Scientific Data, 6(1), 1–5. https://doi.org/10.1038/sdata.2019.38

39.

South

(2017). rnaturalearthdata: World vector map data from natural earth used in ‘rnaturalearth’ [software]. https://cran.r-project.org

40.

South

(2018). rnaturalearth: World map data from natural earth [software]. https://cran.r-project.org

41.

Ten Kate

Koster

W. D.

Van der Waal

. (2021). ‘Following your gut’ or ‘Questioning the scientific evidence’: Understanding vaccine skepticism among more-educated dutch parents. Journal of Health and Social Behavior, 62(1), 85–99. https://doi.org/10.1177/0022146520986118

42.

The jamovi project. (2021). jamovi (Version 1.6) [software]. https://www.jamovi.org

43.

Tran

B. X.

Boggiano

V. L.

Nguyen

L. H.

Latkin

C. A.

Nguyen

H. L.

Tran

T. T.

H.T.

T. T. M.

C. S.

R. C.

(2018). Media representation of vaccine side effects and its impact on utilization of vaccination services in Vietnam. Patient Preference and Adherence, 12, 1717–1728. https://doi.org/10.2147/PPA.S171362

44.

United Nations Development Programme. (2020). Human Development Reports 2020. https://hdr.undp.org/en/2020-report

45.

Wickham

(2016). ggplot2: Elegant graphics for data analysis [software]. Springer. https://ggplot2.tidyverse.org

46.

Viana

van Dorp

C. H.

Nunes

Gomes

M. C.

van Boven

Kretzschmar

M. E.

Veldhoen

Rozhnova

(2021). Controlling the pandemic during the SARS-CoV-2 vaccination rollout. Nature Communications, 12(1), 1–5. https://doi.org/10.1038/s41467-021-23938-8

47.

Vikram

Vanneman

Desai

(2012). Linkages between maternal education and childhood immunization in India. Social Science & Medicine, 75(2), 331–339. https://doi.org/10.1016/j.socscimed.2012.02.043

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