Abstract
Background
Understanding and minimizing existing global coronavirus disease 2019 (COVID-19) vaccination disparities is critical to global population health and eliminating health inequities. The study aims to investigate the disparities of vaccination coverage and progression and the associated economic and educational determinants to inform global COVID-19 vaccination strategies.
Methods
COVID-19 vaccination coverage data from 206 countries used in the study were derived from “Our World in Data” website. After obtaining the vaccination coverage indicators, we fitted the progression indicators for vaccination. Correlation and multiple linear regression analysis were used to examine the effects of gross domestic product (GDP) per capita, Gini index, education, and their interactions on the coverage and progression of the COVID-19 vaccination.
Results
The coverage of COVID-19 vaccination ranged from less than 30 doses to more than 150 doses per hundred people, from less than 15% to more than 75% for proportion of people vaccinated, from less than 15% to more than 60% for proportion of people fully vaccinated. Similarly, the progression of vaccination ranged from less than 0.1 to more than 0.6 for progression of total number of doses, from less than 0.1 to more than 0.3 for progression of proportion of people vaccinated, and from less than 0.1 to more than 0.4 for progression of proportion of people fully vaccinated. GDP per capita and education were positively associated with the coverage and progression, while Gini index was negatively associated with the coverage and progression. Negative interaction between GDP per capita and education was also observed for coverage (β = −0.012 to −0.011, P < 0.05) and progression (β = −0.012 to −0.011, P < 0.05).
Conclusions
Substantial geographic disparities existed for the coverage and progression of COVID-19 vaccination. Economy and education are two important factors contributing to the disparities. Different countries may adopt varied strategies to promote the national distribution and vaccination of COVID-19 vaccines.
Keywords: Coronavirus disease 2019 (COVID-19), Vaccination, GDP per capita, Gini index, Education
1. Introduction
The coronavirus disease 2019 (COVID-19) has caused more than 279 million confirmed cases and over 5 million deaths globally.1 The COVID-19 vaccines are critical cost-effective tools to provide effective protection to all populations and are effective in reducing mortality and preventing serious infections caused by COVID-19 virus strains.2, 3 On October 7, 2021, The United Nations (UN) and World Health Organization (WHO) set the targets that the world can and must meet WHO targets to vaccinate 40% of the population of all countries by the end of 2021 and 70% by mid-2022.4 Although at least 8 billion doses of COVID-19 vaccines have been administered worldwide by December 2021,1 great geographic disparities of supply and the coverage of COVID-19 vaccine across the world were observed, with North America, Europe, and East Asia having administered most of the vaccine doses. In general, high-income countries have administered a large proportion of vaccines. For example, by January 2022, more than 500 million doses of vaccines have been administered in the United States, accounting for 5% of the total number of vaccines administered worldwide and 61% of its population has been fully vaccinated.5 On the other side, in low-and-middle-income countries, such as Chad, the total number of vaccination doses did not exceed 400 000, and only no more than 1% of people are fully vaccinated.4 It is far from the target set by the UN and WHO. Meanwhile, persistent vaccination disparities across countries make it difficult to meet the crucial goal of equitable access to safe and effective vaccines, which is critical in reducing the impact of the disease. Future COVID-19 vaccination strategies should be targeted by global public health evidence.
There are many factors associated to the geographic disparities of the COVID-19 vaccination. One of the most important factors is the gross domestic product (GDP). Countries with higher GDP have more access to and have occupied more doses of vaccines.6 Gini index, another economic indicator, reflects the distribution of income in a country. Previous studies have shown that income imbalance in a country or region is an important influence on vaccination.7 One ecological study showed that the Gini index may be a negative factor for vaccination.8 Education is often regarded as a macro-determinant of national population health and behavior. People in the countries with longer years of schooling have more access to enhance health literacy, which may affect the vaccination progress.9 However, to the best of our knowledge, no study has investigated the effects of education on COVID-19 vaccine coverage and vaccination progress at the national level. Further, in addition to the coverage, the vaccine progression (e.g., speed of vaccination) may greatly influence the COVID-19 pandemic. Few studies have investigated this issue.
The study aims to: (1) investigate the geographic disparities of the coverage and progression of COVID-19 vaccination; (2) examine the effects of economic and educational indicators on the vaccine coverage and progression through ecological research methods; (3) explore the interaction effect of economic and educational indicators on the coverage and progression of COVID-19 vaccination. The ultimate goal of the study is to provide important global health evidence for promoting equitable global access to COVID-19 vaccine and for possible new disease pandemics or the implementation of new vaccines in the future.
2. Methods
2.1. Data source
Data of vaccination by countries were derived from “Our World in Data” website,6 , 10 which were originally collected from the most recent official reports from governments worldwide, from January 1, 2020 to October 1, 2021. Three vaccination coverage indicators used in this study to describe the coverage of COVID-19 vaccination by countries by October 1, 2021, were: (1) Total number of doses, which is the total COVID-19 vaccine doses administered per hundred people of the total population in different vaccination schedules. Administered doses refers to single doses, and may not equal the number of people vaccinated. So total number of doses may exceed 100. (2) Proportion of people vaccinated, which is the number of people who have received at least one COVID-19 vaccine dose per hundred people in the total population. Regardless of the original vaccination schedule, as long as someone receives one dose, it is counted as 1. If they receive the second dose or more, the metric stays the same. (3) Proportion of people fully vaccinated, which is the number of people who have received all doses prescribed by the COVID-19 vaccination protocol per hundred people in total population. Our study used COVID-19 vaccination coverage indicators consistent with data published by WHO.1 If data on coverage are not available on October 1, the latest updated data are collected and the time to look backward is typically 1 week, but in some countries with low vaccination coverage, this time is extended to one month. Such a delay is acceptable because in these low countries the difference in time of one month is negligible. The data for the study involved a partial booster dose, but on October 1, 2021, this is a very low percentage for most countries.
Data of GDP per capita was derived from the most recent year available in “Our World in Data” website10 and was used to measure the economic level of the country and region. Gini index data in 2019 were derived from “Our World in Data” website,10 which was originally from the World Bank inequality data.11 Gini index was used to measure the economic inequality of the country. Countries with higher levels of the Gini index have a greater imbalance in the economy. Education was measured by using mean school years, which were derived from Human Development Reports of United Nations Development Programme.12 It is the average number of years of education received by people ages 25 and older, with longer years of schooling indicating higher level of education.
2.2. Measures
In addition to the three vaccinations coverage indicators and three predictors (GDP per capita, Gini index, education) introduced above, we fitted vaccination progression indicators for each country. We created a linear regression by using the most recent available COVID-19 vaccination coverage data as of October 1 as the dependent variable and the interval (number of days) between the corresponding time and the start of the vaccination process in this country as the independent variable. The slope in the obtained regression equation was used to measure the progression. Corresponding to the three vaccination coverage indicators, we calculated three vaccination progression indicators including: (1) Progression of total number of doses; (2) Progression of proportion of people vaccinated; and (3) Progression of proportion of people fully vaccinated. The higher the value of the vaccination progression indicator, the faster the progress of the corresponding coverage indicator in the country. The slope of time and COVID-19 vaccination is a more reliable indicator since it reflects differences in the progression of COVID-19 vaccinations across countries, compared to the average speed of vaccination (per month) across countries that we have calculated. The average speed of vaccination (per month) is more susceptible to untimely vaccination reporting and overestimate differences in vaccination progression between countries.
2.3. Statistical analysis
To meet the requirements of correlation analysis and multivariate analysis, we did a normalized transformation of the data for all variables using R Package “bestNormalize”. Pearson correlations analysis was conducted to examine the correlation between the six indicators of COVID-19 vaccinations coverage and progression with GDP per capita, Gini index, and education. Further, we used outcome variables transformed by normality for multivariate analysis. Multiple linear regression analysis was used to analyze the relationship between economic and educational determinants and vaccination coverage and progression. We used three coverage indicators and three progression indicators as six different dependent variables and GDP per capita, Gini index, and education as independent variables through six different models, and Model 6 examined the interaction effects between different factors. All analyses were conducted using RStudio software (version 4.1.1). Statistical significance was set at P < 0.05.
3. Results
3.1. Global pattern of COVID-19 vaccination coverage
Our results shows that the total number of doses per hundred people ranged from less than 30 doses in most African countries (e.g., Chad, Kenya, Sudan) to more than 150 doses in countries such as Chile. Countries in Europe, East Asia, North America had more doses of vaccination than other regions. The proportion of people vaccinated ranged from less than 15% in most African countries (e.g., Sudan) to more than 75% in China, Canada, the UK, Chile, etc. Countries in Europe, East Asia, North and South American had a relatively higher proportion of people vaccinated. Likewise, the proportion of people fully vaccinated ranged from less than 15% in most African countries (e.g., Niger, Mali) to more than 60% in Spain and China. Countries in Europe, East Asia, North America had higher coverage of the proportion of people fully vaccinated.
3.2. Global pattern of COVID-19 vaccination progression
We calculated the global pattern of COVID-19 vaccination progression in different countries. Results shows that the progression of total number of doses ranged from less than 0.1 in most African countries (e.g., Chad, Kenya) to more than 0.6 in countries such as China, Canada. Countries in Europe, East Asia, North and South America have higher progression of total number of doses. The progression of proportion of people vaccinated ranged from less than 0.1 in most African countries (e.g., Sudan) to more than 0.3 in China, the UK, Chile. Countries in Europe, East Asia, North and South American, had a higher progression of the proportion of people vaccinated. The progression of proportion of people fully vaccinated ranged from less than 0.1 in most African countries (e.g., Niger, Mali) to more than 0.4 in Saudi Arabia, China. Countries in Europe, East Asia, North America had a higher progression of proportion of people fully vaccinated. Unlike the previous, we have an interesting finding that in a few countries (e.g., China, Saudi Arabia, Uruguay), progression of proportion of people fully vaccinated is higher than progression of proportion of people vaccinated.
3.3. Correlation among GDP per capita, Gini index, education, and the coverage and progression of COVID-19 vaccinations
The results of correlation analysis in Table 1 show that GDP per capita was positively correlated with three vaccination coverage indicators (total number of doses, proportion of people vaccinated, proportion of people fully vaccinated) and three vaccination progression indicators (progression of total number of doses, progression of proportion of people vaccinated, progression of proportion of people fully vaccinated). Gini index was negatively correlated with three vaccination coverage indicators and three vaccination progression indicators. Education was positively correlated with three vaccination coverage indicators and three vaccination progression indicators. More detailed results can be found in Table 1.
Table 1.
Correlation among GDP, Gini index, education, and the coverage and progression of COVID-19 vaccinations.
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|
| 1 | ||||||||
| 2 | −0.43* | |||||||
| 3 | 0.79* | −0.45* | ||||||
| 4 | 0.79* | −0.36* | 0.68* | |||||
| 5 | 0.77* | −0.33* | 0.65* | 0.98* | ||||
| 6 | 0.81* | −0.40* | 0.69* | 0.97* | 0.96* | |||
| 7 | 0.76* | −0.36* | 0.69* | 0.95* | 0.95* | 0.94* | ||
| 8 | 0.75* | −0.32* | 0.69* | 0.90* | 0.92* | 0.88* | 0.95* | |
| 9 | 0.73* | −0.38* | 0.62* | 0.88* | 0.88* | 0.90* | 0.92* | 0.85* |
All correlation analyses were performed using normally transformed data. 1: GDP per capita; 2: Gini index; 3: Education; 4: Total number of doses; 5: Proportion of people vaccinated; 6: Proportion of people fully vaccinated; 7: Progression of total number of doses; 8: Progression of proportion of people vaccinated; 9: Progression of proportion of people fully vaccinated. *P < 0.05.
3.4. Effects of GDP, Gini index, and education on the coverage and progression of COVID-19 vaccinations
Model 1 used GDP per capita as the independent variable and different vaccination indicators as the dependent variable, and results show that GDP per capita was significantly and positively associated with the coverage (β = 0.030 to 0.036, P < 0.05) and progression (β = 0.028 to 0.030, P < 0.05) of COVID-19 vaccination. Model 2 used Gini index as the independent variable, results present that the Gini index was significantly and negatively associated with the coverage (β = −0.048 to −0.041, P < 0.05) and progression (β = −0.045 to −0.037, P < 0.05) in COVID-19 vaccination. Model 3 included GDP per capita and Gini index, indicates that the Gini index was not significantly associated with the coverage and progression of COVID-19 vaccination after controlling for GDP per capita, except that the Gini index was negatively associated with the progression of proportion of people fully vaccinated (β = −0.019, 95% CI: −0.036 to −0.002). Model 4 with education as the independent variable depicts that education was significantly and positively associated with the coverage (β = 0.207 to 0.220, P < 0.05) and progression (β = 0.198 to 0.215, P < 0.05) of COVID-19 vaccination. When GDP per capita, Gini index, and education were included, GDP per capita and education were significantly associated with the coverage and progression of COVID-19 vaccination (Model 5). In addition, there was a significant and negative interaction between GDP per capita and education (Model 6) for coverage (β = −0.012 to −0.011, P < 0.05) and progression (β = −0.012 to −0.011, P < 0.05) of vaccinations, more details can be seen in Table 2 .
Table 2.
Coefficients and confidence intervals from multiple linear regression using GDP, Gini index, and education to predict the coverage and progression of COVID-19 vaccinations [β (95% CI)].
| Model | Total number of doses | Proportion of people vaccinated | Proportion of people fully vaccinated | Progression of total number of doses | Progression of proportion of people vaccinated | Progression of proportion of people fully vaccinated |
|---|---|---|---|---|---|---|
| Model 1 | ||||||
| GDP | 0.030 (0.025 to 0.035)※ | 0.032 (0.027 to 0.037)※ | 0.036 (0.030 to 0.041)※ | 0.030 (0.025 to 0.035)※ | 0.030 (0.025 to 0.035)※ | 0.028 (0.023 to 0.034)※ |
| R2 | 0.463 | 0.435 | 0.490 | 0.413 | 0.407 | 0.351 |
| Model 2 | ||||||
| Gini index | −0.041 (−0.059 to −0.023)※ | −0.040 (−0.059 to −0.021)※ | −0.048 (−0.066 to −0.029)※ | −0.041 (−0.059 to −0.023)※ | −0.037 (−0.056 to −0.019)※ | −0.045 (−0.063 to −0.027)※ |
| R2 | 0.122 | 0.099 | 0.146 | 0.119 | 0.092 | 0.136 |
| Model 3 | ||||||
| GDP | 0.041 (0.034 to 0.048)※ | 0.041 (0.034 to 0.049)※ | 0.048 (0.041 to 0.055)※ | 0.037 (0.030 to 0.044)※ | 0.037 (0.029 to 0.044)※ | 0.031 (0.023 to 0.039)※ |
| Gini index | −0.007 (−0.022 to 0.008) | −0.003 (−0.019 to 0.012) | −0.007 (−0.021 to 0.007) | −0.010 (−0.025 to 0.005) | −0.006 (‒0.022 to 0.010) | −0.019 (−0.036 to −0.002)※ |
| R2 | 0.525 | 0.507 | 0.606 | 0.473 | 0.433 | 0.376 |
| Model 4 | ||||||
| Education | 0.214 (0.178 to 0.250)※ | 0.207 (0.170 to 0.244)※ | 0.220 (0.184 to 0.255)※ | 0.215 (0.180 to 0.250)※ | 0.214 (0.179 to 0.248)※ | 0.198 (0.160 to 0.236)※ |
| R2 | 0.464 | 0.434 | 0.482 | 0.479 | 0.480 | 0.393 |
| Model 5 | ||||||
| GDP | 0.026 (0.016 to 0.035)※ | 0.027 (0.017 to 0.036)※ | 0.035 (0.025 to 0.045)※ | 0.019 (0.010 to 0.028)※ | 0.017 (0.008 to 0.027)※ | 0.014 (0.003 to 0.025)※ |
| Gini index | −0.001 (−0.015 to 0.014) | 0.004 (−0.012 to 0.019) | −0.003 (−0.017 to 0.011) | −0.002 (−0.016 to 0.013) | 0.003 (−0.011 to 0.018) | −0.013 (−0.030 to 0.004) |
| Education | 0.116 (0.068 to 0.164)※ | 0.107 (0.057 to 0.157)※ | 0.091 (0.043 to 0.140)※ | 0.142 (0.094 to 0.190)※ | 0.152 (0.103 to 0.202)※ | 0.131 (0.075 to 0.187)※ |
| R2 | 0.601 | 0.573 | 0.653 | 0.586 | 0.569 | 0.469 |
| Model 6 | ||||||
| GDP | 0.205 (0.132 to 0.278)※ | 0.233 (0.158 to 0.309)※ | 0.207 (0.139 to 0.276)※ | 0.186 (0.113 to 0.259)※ | 0.177 (0.101 to 0.253)※ | 0.171 (0.084 to 0.257)※ |
| Gini index | −0.021 (−0.068 to 0.026) | −0.019 (−0.067 to 0.030) | −0.032 (−0.076 to 0.012) | −0.006 (−0.054 to 0.041) | −0.004 (−0.054 to 0.045) | −0.037 (−0.093 to 0.019) |
| Education | 0.010 (−0.240 to 0.260) | −0.027 (−0.284 to 0.229) | −0.039 (−0.272 to 0.195) | 0.131 (−0.119 to 0.381) | 0.125 (−0.136 to 0.386) | 0.048 (−0.248 to 0.344) |
| GDP* Gini index | −0.001 (−0.003 to 0.000) | −0.002 (−0.003 to 0.000)※ | −0.001 (−0.002 to 0.001) | −0.001 (−0.002 to 0.001) | −0.001 (−0.002 to 0.001) | 0.001 (−0.002 to 0.001) |
| GDP* Education | −0.011 (−0.015 to −0.008)※ | −0.012 (−0.016 to −0.008)※ | −0.011 (−0.015 to −0.008)※ | −0.012 (−0.015 to −0.008)※ | −0.011 (−0.015 to ‒0.007)※ | −0.011 (−0.016 to −0.007)※ |
| Gini index* Education | 0.004 (−0.003 to 0.11) | 0.005 (−0.002 to 0.012) | 0.004 (−0.002 to 0.011) | 0.001 (−0.006 to 0.008) | 0.002 (−0.006 to 0.009) | 0.003 (−0.005 to 0.011) |
| R2 | 0.696 | 0.682 | 0.750 | 0.683 | 0.656 | 0.574 |
Total number of doses, proportion of people vaccinated, proportion of people fully vaccinated, progression of total number of doses, progression of proportion of people vaccinated, progression of proportion of people fully vaccinated are counted in units per 100 people. Models 1‒5 separately examined effects of GDP per capita, Gini index, and education. Model 6 examined the interaction effects between different factors. β: Unstandardized coefficients; CI: Confidence interval; R2: Adjusted R-squared. ※P < 0.05.
4. Discussion
In this ecological study, we identified great geographic disparities in the coverage and progression of COVID-19 vaccination worldwide a finding that could contribute to the development of a comprehensive COVID-19 vaccination advancement strategy. We further investigated the relationship between economy and education at the national level with the coverage and progression of COVID-19 vaccination. Findings of the study provide new evidence to deepen our understanding of the global distribution and associated factors of COVID-19 vaccination. Most importantly, our results provide a valuable model for possible future pandemics, as the education and economic levels of different countries will not change for a long time. When new vaccines are implemented as emergency tools, it is necessary to consider not only vaccine resources determined by economic factors, but also vaccine acceptability influenced by education.
Study findings show that countries in North America, Europe, and East Asia had higher COVID-19 vaccine coverage than countries in other regions. The possible explanation may be related to the economic power (e.g., the United States) and industrial capacity (e.g., China) owned by these countries.13, 14 Meanwhile, there are also a few exceptions, such as Chile and Brazil. The success of the vaccination campaigns in Brazil15 and Chile16 may be attributable to the unbreakable vaccine culture, which means longstanding national advocacy for vaccines, a trusted public health system, resident trust, community relationship building, etc. In these countries, the severity of the early epidemic may also have played a strong role in vaccination such as Brazil.17Previous studies have found that higher risk perceptions of getting infected is an important factor driving vaccination. Trust in vaccination is also playing an important role.18 Due to the rapid of the epidemic, COVID-19 vaccines are being developed much faster than those traditionally administered. This has led to more concerns about vaccines, and different countries have dealt with this issue in different ways (e.g., in terms of scientific awareness, health surveillance efforts) are also important reasons for the differences in vaccination. Study findings also showed that countries in Europe, North America, South America, East Asia had quicker progression of vaccination. The fast COVID-19 vaccination progression can be attributed to two main reasons including adequate sources of vaccine and high vaccine acceptance. Vaccine resources can be explained more by higher national vaccine purchasing power (e.g., Saudi Arabia), strong geopolitics (e.g., Canada), and a well-established vaccine manufacturing system (e.g., China),1 with the economy being the underlying factor. Vaccine acceptance can be explained more by people's awareness, behavior, and attitudes, and education is one of the most important factors in determining these.19 In addition, several countries showed a much quicker progression of proportion of people fully vaccinated, such as China, Saudi Arabia, and Uruguay, which may be related to higher national trust in government18 , 20 and their earlier start of the COVID-19 vaccination process and earlier initiation of the second dose vaccination.
Findings of the study also indicated that countries with higher GDP per capita, longer years of education, and lower Gini index had more vaccination doses and faster vaccination progression. The impact of GDP was consistent with previous studies.6 , 8 It is well-known that countries with higher GDP may have more sources of vaccines and well-developed mechanisms to promote vaccination.21 The same phenomenon can be seen in the 2019-H1N1 influenza outbreak that developing countries identified the lack of equity in how developed countries were securing access to the vaccine.22 Poor countries also have great difficulty in setting up cold chain systems to serve vaccines, to the extent that they cannot guarantee vaccination of their people.23 If this phenomenon is not taken seriously, there is no doubt that history will repeat itself the next time when a new epidemic breaks out. We also found that the Gini index exerted negative effects on vaccination in the univariate analysis. Increased income inequality may indirectly affect a society's political and economic institutions and other aspects that influence vaccination.24 After adjusting for GDP and education, the Gini index did not show significant effects, which may be associated with the potential mediation mechanisms. Future studies are needed to test this possible mechanism. Our findings are consistent with some recent individual-level surveys,25, 26 which reported that higher levels of education are associated with greater willingness to be vaccinated. One reason may be that the longer mean school years in a country, the more comprehensive the perception of the risks associated with an epidemic and the more willing to vaccinate.27 In addition, the longer mean school years of people, the easier it is to overcome the narrow-mindedness of religion and ignorance.28 The longer mean school years bring more and more human resources to promote the construction of public health institutions and the implementation of vaccination is also one of the potential reasons.
Finally, we identified that education can interact with GDP per capita at the country level to substantially weaken the positive relationship between economy and COVID-19 vaccination. The mechanism of the interaction is not yet clear. In two reviews, it was shown that high education was associated with high vaccine acceptance in both high-income29 and low- and middle-income countries.19 Our results don't conflict with that. Meanwhile, the negative interaction between education and economy on COVID-19 vaccination still exists. The positive effect of education on vaccination seems to be diminished in developed countries, but this has not been found by previous studies. The interaction can be explained by the following points. First of all, in countries with poor economies, more educated people have a better understanding of vaccines and a more objective perception of diseases, making them more willing to get vaccinated.30 However, in countries with advanced economies, distrust of the government and the difficulty of enforcing mandatory vaccination for educated populations are two important reasons why people's vaccination have declined.18 , 31 One study found that primary school completion was associated with vaccine coverage in African countries, but socioeconomic factors such as education do not appear to have a correlation with vaccination rates in previous studies (both recently and historically).32 It means that in many developed countries in Europe, education has not been able to promote vaccine universalization. In surprising agreement with our findings, this suggests that there may be potential differences in the sensitivity of the effect of education on vaccination in countries with different economic levels. In addition, it was also found33 that women aged 30–49 years and men with higher education levels were less likely to be vaccinated in developed countries such as Japan. It suggests that differences in the demographic composition of different countries are responsible for the interaction. And in Japan, the more educated people are less likely to be vaccinated also supported our results on a personal level.
To our knowledge, this is the first ecological study that investigated the relationship between the educational factor and COVID-19 vaccination and reported on the progression of the vaccination process and the factors influencing it by country. The time period we chose for observation well avoids the emergence of new strains (Omicron), the impact of changes in vaccine efficacy and adjustment in national public health policies on vaccination. And we observed that the negative interactions between economics and education. Although numerous studies have investigated the economic factors related to vaccination. However, they were either investigated from an individual perspective or focus on vaccine coverage at a particular point in time. COVID-19 pandemics is providing a valuable model in many different fields of medicine. In this case, the implementation of a new vaccine and its global determinants are of great importance also for future preparedness pandemic plans. Our study fills a gap in vaccination ecological research related to education and supports the subsequent vaccination process.
This study has some limitations. Firstly, country-level associations may not be generalizable to regions or communities and results could not be extrapolated to the individual-level association. Secondly, partial absence of data and untimely reporting of data were also sources of study bias. Thirdly, we use the mean school years to reflect the overall education level of the country, which only represents the group over 25 years old, those under age 25 are not considered, in addition to the vaccination data is also based on the population over the age of 18, the slight differences brought by different populations need to be further examined.
In conclusion, with the COVID-19 pandemic and the advent of mass vaccination, our findings raise concerns about inequities in vaccination, particularly in countries that experience backwardness and low education. Different countries may adopt varied strategies in promoting COVID-19 vaccination. Further exploration is needed to focus on these countries to ensure vaccination coverage and control of the epidemic. Our study also found huge differences in COVID-19 vaccination progressions across countries worldwide. The results of this study further validate the positive effect of the economy on vaccination and indicate the urgency of further research related to the complex mechanisms of potential economic and educational influences on vaccination at the individual level.
Acknowledgments
Acknowledgments
We would like to acknowledge and thank Hannah Ritchie, Edouard Mathieu, Lucas Rodés-Guirao, Cameron Appel ‘s team who collectively contributed to COVID-19 vaccination data collation, collection, and publication.
Availability of data and materials
This study used publicly available data that can be found online on their respective repositories. The compiled datasets, analysis files, and logs produced for this study are available from the corresponding author upon request.
Competing interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Consent for publication
Our research or manuscript don't contain any individual person's data in any form (including any individual details, images or videos).
CRediT authorship contribution statement
Huihao Wang: Data curation, Writing ... original draft, Visualization. Bin Yu: Conceptualization, Data curation, Funding acquisition, Supervision. Xinguang Chen: Methodology, Supervision. Hong Yan: Project administration, Validation, Writing ... review & editing.
Ethics approval and consent to participate
The study was secondary data analysis. No personal interests are involved.
Funding
This work was supported by the starting funding package from Wuhan University (PI: Bin Yu). The sponsors had no role in the design and conduct of this study.
References
- 1.WHO coronavirus (COVID-19) dashboard. World Health Organization website. https://covid19.who.int. Accessed December 28, 2021.
- 2.Knoll MD, Wonodi C. Oxford-AstraZeneca COVID-19 vaccine efficacy. Lancet. 2021;397(10269):72–74. doi: 10.1016/S0140-6736(20)32623-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Fiolet T, Kherabi Y, MacDonald CJ, Ghosn J, Peiffer-Smadja N. Comparing COVID-19 vaccines for their characteristics, efficacy and effectiveness against SARS-CoV-2 and variants of concern: a narrative review. Clin Microbiol Infect. 2022;28(2):202–221. doi: 10.1016/j.cmi.2021.10.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.WHO, UN set out steps to meet world COVID vaccination targets. World Health Organization website. https://www.who.int/news/item/07-10-2021-who-un-set-out-steps-to-meet-world-covid-vaccination-targets. Accessed December 18, 2022.
- 5.WHO coronavirus (COVID-19) dashboard. World Health Organization Website https://covid19.who.int. Accessed January 16, 2022.
- 6.Mathieu E, Ritchie H, Ortiz-Ospina E, et al. A global database of COVID-19 vaccinations. Nat Hum Behav. 2021;5(7):947–953. doi: 10.1038/s41562-021-01122-8. [DOI] [PubMed] [Google Scholar]
- 7.Caspi G, Dayan A, Eshal Y, et al. Socioeconomic disparities and COVID-19 vaccination acceptance: a nationwide ecologic study. Clin Microbiol Infect. 2021;27(10):1502–1506. doi: 10.1016/j.cmi.2021.05.030. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Sobral MFF, Bezerra de Oliveira BR, Gomes da Penha Sobral AI, Monteiro Marinho ML, Duarte GB, de Souza Melo A. SARS-COV-2 vaccines: what indicators are associated with the worldwide distribution of the first doses. Inquiry. 2021;58 doi: 10.1177/00469580211060184. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Götmark F, Andersson M. Human fertility in relation to education, economy, religion, contraception, and family planning programs. BMC Public Health. 2020;20(1):265. doi: 10.1186/s12889-020-8331-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Coronavirus (COVID-19) vaccinations. Our World in Data website. https://ourworldindata.org/covid-vaccinations. Accessed October 6, 2022.
- 11.Gini index. The World Bank Website https://data.worldbank.org/indicator/SI.POV.GINI. Accessed October 5, 2021.
- 12.Human development insights. United Nations Development Programme website. https://hdr.undp.org/data-center/country-insights. Accessed October 5, 2021.
- 13.Xu M, Liang Z, Xu Y, Wang J. Chinese vaccine products go global: vaccine development and quality control. Expert Rev Vaccines. 2015;14(5):763–773. doi: 10.1586/14760584.2015.1012503. [DOI] [PubMed] [Google Scholar]
- 14.Pagliusi S, Jarrett S, Hayman B, et al. Emerging manufacturers engagements in the COVID -19 vaccine research, development and supply. Vaccine. 2020;38(34):5418–5423. doi: 10.1016/j.vaccine.2020.06.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Opinion: in Brazil's successful vaccine campaign, a lesson for the U.S. Undark website. https://undark.org/2021/10/14/in-brazil-successful-vaccine-campaign-lesson-for-us/. Accessed October 8, 2021.
- 16.Castillo C, Villalobos Dintrans P, Maddaleno M. The successful COVID-19 vaccine rollout in Chile: factors and challenges. Vaccine X. 2021;9 doi: 10.1016/j.jvacx.2021.100114. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Santos AMD, Souza BF de, Carvalho CA de, et al. Excess deaths from all causes and by COVID-19 in Brazil in 2020. Rev Saude Publica. 2021;55:71. doi: 10.11606/s1518-8787.2021055004137. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Lazarus JV, Ratzan SC, Palayew A, et al. A global survey of potential acceptance of a COVID-19 vaccine. Nat Med. 2021;27(2):225–228. doi: 10.1038/s41591-020-1124-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Bono SA, Faria de Moura Villela E, Siau CS, et al. Factors affecting COVID-19 vaccine acceptance: an international survey among low- and middle-income countries. Vaccines. 2021;9(5):515. doi: 10.3390/vaccines9050515. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Taylor L. Why Uruguay lost control of COVID. Nature. 2021;595(7865):21. doi: 10.1038/d41586-021-01714-4. [DOI] [PubMed] [Google Scholar]
- 21.Solís Arce JS, Warren SS, Meriggi NF, et al. COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries. Nat Med. 2021;27(8):1385–1394. doi: 10.1038/s41591-021-01454-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Fidler DP. Negotiating equitable access to influenza vaccines: global health diplomacy and the controversies surrounding avian influenza H5N1 and pandemic influenza H1N1. PLoS Med. 2010;7(5) doi: 10.1371/journal.pmed.1000247. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Najjar H, Al-Jighefee HT, Qush A, Ahmed MN, Awwad S, Kamareddine L. COVID-19 vaccination: the mainspring of challenges and the seed of remonstrance. Vaccines. 2021;9(12):1474. doi: 10.3390/vaccines9121474. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Truesdale BC, Jencks C. The health effects of income inequality: averages and disparities. Annu Rev Public Health. 2016;37:413–430. doi: 10.1146/annurev-publhealth-032315-021606. [DOI] [PubMed] [Google Scholar]
- 25.Fisher KA, Bloomstone SJ, Walder J, Crawford S, Fouayzi H, Mazor KM. Attitudes toward a potential SARS-CoV-2 vaccine : a survey of U.S. adults. Ann Intern Med. 2020;173(12):964–973. doi: 10.7326/M20-3569. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Scherer AM, Gedlinske AM, Parker AM, et al. Acceptability of adolescent COVID-19 vaccination among adolescents and parents of adolescents−United States, April 15‒23, 2021. MMWR Morb Mortal Wkly Rep. 2021;70(28):997–1003. doi: 10.15585/mmwr.mm7028e1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Rzymski P, Zeyland J, Poniedziałek B, Małecka I, Wysocki J. The perception and attitudes toward COVID-19 vaccines: a cross-sectional study in Poland. Vaccines (Basel) 2021;9(4):382. doi: 10.3390/vaccines9040382. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Galang JRF. Science and religion for COVID-19 vaccine promotion. J Public Health (Oxf) 2021;43(3):e513–e514. doi: 10.1093/pubmed/fdab128. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Aw J, Seng JJB, Seah SSY, Low LL. COVID-19 vaccine hesitancy-a scoping review of literature in high-income countries. Vaccines (Basel) 2021;9(8):900. doi: 10.3390/vaccines9080900. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Wong LP, Alias H, Danaee M, et al. COVID-19 vaccination intention and vaccine characteristics influencing vaccination acceptance: a global survey of 17 countries. Infect Dis Poverty. 2021;10(1):122. doi: 10.1186/s40249-021-00900-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Peters MDJ. Addressing vaccine hesitancy and resistance for COVID-19 vaccines. Int J Nurs Stud. 2022;131 doi: 10.1016/j.ijnurstu.2022.104241. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.de Figueiredo A, Johnston IG, Smith DMD, Agarwal S, Larson HJ, Jones NS. Forecasted trends in vaccination coverage and correlations with socioeconomic factors: a global time-series analysis over 30 years. Lancet Glob Health. 2016;4(10):e726–e735. doi: 10.1016/S2214-109X(16)30167-X. [DOI] [PubMed] [Google Scholar]
- 33.Ishimaru T, Okawara M, Ando H., et al. Gender differences in the determinants of willingness to get the COVID-19 vaccine among the working-age population in Japan. Hum Vaccin Immunother. 2021;17(11):3975–3981. doi: 10.1080/21645515.2021.1947098. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
This study used publicly available data that can be found online on their respective repositories. The compiled datasets, analysis files, and logs produced for this study are available from the corresponding author upon request.