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Disparities in coronavirus disease 2019 (COVID-19) vaccination coverage among countries have been observed following the introduction of COVID-19 vaccines, which has hindered global efforts to control the pandemic. The 80% of the world’s population only had access to approximately 5% of the total COVID-19 vaccines available (1), and vaccination rate per capita in high-income countries is approximately 7.8 times higher than that in low-income countries (2), leading to prolonged impacts of the pandemic (1,3). In addition to the financial status of individuals or countries, there could be additional factors that influence people’s willingness to receive the vaccine, which public health agencies might consider or address. Gaining insights into the underlying reasons behind COVID-19 vaccine uptake would enhance global preparedness for the potential pandemics in the future.
There is a need to better understand the factors contributing to disparities in COVID-19 vaccination rates across countries. This goes beyond the economic reasons that have been previously studied (4–5). Therefore, our study aims to identify the country-level predictors that contribute to global disparities in COVID-19 vaccine coverage.
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As of July 2022, COVID-19 vaccination coverage varied widely among the 219 countries and territories, ranging from 0.13% to 126.79% (Figure 1). COVID-19 vaccination coverage was positively correlated with delivered doses per capita and vaccine acceptance (Figure 2). Note that despite some countries having high vaccine delivery per capita (e.g., Austria, Liechtenstein, Hungary, Dominica, Saint Vincent and the Grenadines, and Djibouti) or high vaccine acceptance rates (e.g., Somalia, Bosnia and Herzegovina, Paraguay, Iran, Nepal, Indonesia, and Brazil), they still experienced low COVID-19 vaccination coverage.
Figure 1.Full COVID-19 vaccination coverage in six WHO regions as of July 19, 2022. (A) Africa; (B) Americas; (C) South-east Asia; (D) Eastern mediterranean; (E) Western Pacific; (F) Europe.
Note: Since vaccination is available to both residents and non-residents such as tourists and foreign workers, vaccination coverage in some countries or territories may exceed 100%. Tonga (vaccine coverage rate of 91.64%) is not shown separately due to its location in Oceania. Full COVID-19 vaccination coverage rates in the regions of Hong Kong SAR, Macau SAR, and Taiwan, China are 86.61%, 85.51%, and 83.16%, respectively
Abbreviation: WHO=World Health Organization; COVID-19=coronavirus disease 2019; SAR=Special Administrative Region.
Figure 2.Correlations between COVID-19 vaccination coverage and vaccine delivery/acceptance as of July 19, 2022. (A) vaccine delivery; (B) vaccine acceptance.
Abbreviation: COVID-19=coronavirus disease 2019; CI=confidence interval.Univariate linear regressions showed that significant correlations (P<0.05) were found between COVID-19 vaccination coverage and all country-level indicators analyzed (Table 1). When examining demographic characteristics, a positive association was observed between vaccination coverage and the proportion of urban population (R2=24.3%), and the proportion of population aged 65 and above (R2=27.2%). In contrast, a negative association was observed with the proportion of population aged 0−14 (R2=51.4%). In terms of social development indicators, vaccination coverage showed positive associations with the socio-demographic index (R2=44.2%) and GDP per capita (R2=49.9%). Positive associations were also found with indicators measuring preparedness for pandemics, including the epidemic ready score (R2=55.8%), global health security index (R2=33.3%), number of medical doctors per 1,000 people (R2=30.2%), and vaccine manufacturing capability (R2=4.8%). Among indicators related to governance and trust, vaccination coverage displayed positive correlations with government effectiveness (R2=52.4%), control of corruption (R2=39.6%), trust in government (R2=10.4%), trust in science (R2=35.1%), and interpersonal trust (R2=22.6%). Conversely, state fragility (R2=53.8%) showed a negative correlation. Regarding COVAX, vaccination coverage was negatively associated with participation in COVAX through advance market commitment (R2=28.9%), but not significantly associated with self-financing participation in COVAX.
Variables N Univariate linear regressions Multifactorial regressions Coefficient, 95% CI P-value R2 Coefficient, 95% CI P−value Demographics Urban population, % 196 0.57 (0.43, 0.71) <0.001 24.3% Population aged 65 and above, % 181 2.12 (1.61, 2.63) <0.001 27.2% −0.88 (−2.30, 0.53) 0.214 Population aged 0–14, % 181 −1.89 (−2.16, −1.62) <0.001 51.4% −2.22 (−3.10, −1.34) <0.001 Social development status Socio-Demographic Index 188 99.01 (82.91, 115.10) <0.001 44.2% Ln (GDP per capita) 175 12.90 (10.96, 14.83) <0.001 49.9% Pandemic preparedness Epidemic Ready Score 94 1.09 (0.89, 1.29) <0.001 55.8% 0.50 (0.12, 0.88) 0.011 Global Health Security Index 181 1.12 (0.88, 1.35) <0.001 33.3% Doctors per 1,000 people 189 0.83 (0.65, 1.01) <0.001 30.2% −0.04 (−0.46, 0.37) 0.831 Vaccine manufacturing capability 219 14.46 (5.86, 23.07) 0.001 4.8% −1.39 (−10.74, 7.95) 0.765 Governance Government effectiveness 196 19.16 (16.57, 21.74) <0.001 52.4% Control of corruption 196 16.60 (13.70, 19.50) <0.001 39.6% State fragility 176 −7.79 (−8.87, -6.71) <0.001 53.8% Trust Trust in government, % 106 0.41 (0.18, 0.65) 0.001 10.4% 0.25 (0.03, 0.46) 0.024 Trust in science, % 112 1.22 (0.91, 1.54) <0.001 35.1% Interpersonal trust, % 56 0.72 (0.36, 1.08) <0.001 22.6% COVAX participation Participation in COVAX through advance
market commitment219 −28.09 (−35.95, −20.23) <0.001 28.9% 10.03 (−4.36, 24.41) 0.167 Self-financing participation in COVAX 219 1.20 (−0.66, 9.06) 0.764 28.9% −1.69 (−17.60, 14.22) 0.831 Abbreviation: COVID-19=coronavirus disease 2019; CI=confidence interval; GDP=gross domestic product; COVAX=COVID-19 vaccines global access. Table 1. Associations between country-level characteristics and COVID-19 vaccination coverage by univariate and multifactorial regression.
Following variable selection through LASSO regression, the final variables retained for the multifactorial regression analysis were population aged 65 and above, population aged 0–14, epidemic readiness score, doctors per 1,000 people, vaccine manufacturing capability, trust in government, participation in COVAX through advance market commitment, and self-financing participation in COVAX. In the multifactorial regression, population aged 0–14 [coefficient −2.22, 95% confidence interval (CI): −3.10, −1.34], epidemic readiness score (coefficient 0.50, 95% CI: 0.12, 0.88), and trust in government (coefficient 0.25, 95% CI: 0.03, 0.46) were found to be significant predictors of vaccination coverage when accounting for other factors. However, population aged 65 and above, doctors per 1,000 people, vaccine manufacturing capability, and COVAX index were not found to be significant predictors (Table 1).
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