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Geospatial modelling of COVID-19 vaccination coverage inequalities: evidence from 192 countries

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Abstract

Geospatial modelling is a useful analytical tool for efficient characterization of inequalities in COVID-19 vaccination coverage for effective management control of the pandemic, which is limited in the literature on a global scale. This study investigated the spatial distribution of disparities in COVID-19 vaccine coverage indicators, namely, total vaccination rate (TVR), population proportion with at least one dose (1 + Dose rate), full vaccination rate (FVR), and booster vaccination rate (BVR) globally by accounting for socio-economic and healthcare accessibility indices, which included the number of vaccine types, COVID-19 containment and health index (CHI), universal healthcare service coverage index (USCI), human development index (HDI), and gross domestic product per capita (GDP/capita), as covariates. The analysis used a global dataset on the four vaccine coverage indicators and covariates. In the multivariate analysis, USCI independently predicted each vaccine coverage rate; CHI independently predicted all coverage rates except BVR; GDP/capita independently predicted only BVR; and number of vaccine types independently predicted FVR and 1 + Dose rate. Spatial autocorrelation tests and cokriging predictions produced spatial maps indicating clustering of countries with low coverage rates (0–29/100 people), mostly in the WHO African region and high clustering of TVR and 1 + Dose rates (101–185 and 49–76 per 100 population, respectively) in other parts, mostly in the developed countries. The study findings highlight the importance of global efforts of improving vaccine diplomacy and dose-sharing to address the inequalities in vaccine coverage.

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Data availability

The data that support the findings of this study are openly available in figshare at https://doi.org/10.6084/m9.figshare.21594453

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Acknowledgements

The authors are grateful to the anonymous reviewers and editors for the useful comments and criticisms provided in the peer review process.

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The authors did not receive funding from any organization for the submitted work.

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Authors and Affiliations

  1. Department of Emergency Medicine, Faculty of Medicine and Dentistry, College of Health Sciences, University of Alberta, Edmonton, AB, Canada

    Nana Owusu M. Essel

  2. Department of Statistics and Actuarial Science, College of Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

    Eric Nimako Aidoo & Simon Kojo Appiah

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Contributions

All authors contributed to the study conception and design as well as material preparation, data collection and analysis. The first draft of the manuscript was written by NOME and SKA while ENA edited the cluster and spatial maps. All authors contributed equally in reviewing previous versions of the manuscript, read and approved the final manuscript.

Corresponding author

Correspondence to Simon Kojo Appiah.

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The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

Ethical considerations

This study did not require ethics approval as it involved an ecological study with the use of publicly available aggregate data.

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Essel, N.O.M., Aidoo, E.N. & Appiah, S.K. Geospatial modelling of COVID-19 vaccination coverage inequalities: evidence from 192 countries. Spat. Inf. Res. 31, 653–667 (2023). https://doi.org/10.1007/s41324-023-00531-3

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