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Record W4407728112 · doi:10.1136/bmjgh-2024-017761

Evaluating geographic accessibility to COVID-19 vaccination across 54 countries/regions

2025· article· en· W4407728112 on OpenAlexaboutno aff
Yanjia Cao, Tianyu Li, Huanfa Chen, Qunshan Zhao, Karen A. Grépin, Jeon‐Young Kang

Bibliographic record

VenueBMJ Global Health · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
FundersUniversity Research Committee, University of Hong KongEconomic and Social Research CouncilUniversity of Hong Kong
KeywordsVaccinationPer capitaPopulationGeographyEnvironmental healthPandemicCoronavirus disease 2019 (COVID-19)MedicineSocioeconomicsDemographyVirology

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic has revealed significant disparities in global vaccine accessibility, particularly affecting low and middle-income countries (LMICs). However, current research on COVID-19 vaccine accessibility primarily focuses on individual countries or high-income countries (HIC). We aimed to evaluate geographic accessibility to COVID-19 vaccination on a multicountry scale, covering comparisons across LMICs and HICs. Additionally, we explored the potential economic factors related to accessibility and their impacts on health outcomes. METHODS: We collected population data at a 1 km resolution and geocoded all vaccination sites across the selected countries/regions. Four measures were used to evaluate vaccine accessibility from different perspectives: population coverage with varying travel time thresholds, driving time to vaccination sites, the number of sites within a 30-min threshold and a geographic accessibility index using enhanced two-step floating catchment area method. Finally, we explored the relationships between geographic accessibility and several factors: gross domestic product per capita, vaccination uptake and mortality. FINDINGS: We found substantial disparities in vaccine accessibility across the selected countries/regions. In 24.07% of these countries/regions, over 95% of the population can access the nearest vaccination services within 15 min. In contrast, in countries/regions such as Manitoba (Canada), Zimbabwe and Bhutan, less than 30% of the population can reach these sites within 60 min. Underserved areas, termed 'vaccine deserts', were identified in both HICs and LMICs. We found that countries/regions with higher vaccine accessibility tend to achieve higher vaccination rates, whereas those with lower vaccine accessibility are likely to experience substantial increases in COVID-19 mortality rates. CONCLUSION: LMICs require enhanced attention to improve geographic accessibility to vaccination. Additionally, there are internal disparities in accessibility within both HICs and LMICs. National public health officials and global health initiatives are suggested to prioritize 'vaccine deserts' and to ensure equitable vaccine access in future pandemics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.098
GPT teacher head0.563
Teacher spread0.464 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2025
Admission routes1
Has abstractyes

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