Evidence map›Paper›PMID 41960709›Full record

ArticleJournal of global health2026

Geospatial analysis of COVID-19 vaccine access in Kenya: the interplay of travel time, perceived availability, and vaccine uptake.

Jiashuo Sun, Yanjia Cao, Karen Ann Grépin, Tianyu Li, Huanfa Chen, Qunshan Zhao, Primus Che Chi

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Article in Journal of global health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Jiashuo SunDepartment of Geography, The University of Hong Kong, Pok Fu Lam, Hong Kong, Hong Kong.
Yanjia CaoDepartment of Geography, The University of Hong Kong, Pok Fu Lam, Hong Kong, Hong Kong.
Karen Ann GrépinDivision of Health Economics, Policy and Management, School of Public Health, The University of Hong Kong, Pok Fu Lam, Hong Kong, Hong Kong.
Tianyu LiDepartment of Geography, The University of Hong Kong, Pok Fu Lam, Hong Kong, Hong Kong.
Huanfa ChenCentre for Advanced Spatial Analysis, University College London, London, UK.
Qunshan ZhaoUrban Big Data Centre, School of Social and Political Sciences, University of Glasgow, Glasgow, UK.
Primus Che ChiDepartment of Health Systems and Research Ethics, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: COVID-19 vaccines have reduced severe illness and mortality, yet inequitable distribution delayed rollout in many low- and middle-income countries (LMICs), including Kenya, where only 36% of adults were fully vaccinated by October 2022. While sociodemographic and attitudinal determinants of uptake are well documented, role of geographic access remain underexplored at individual level. This study assessed population coverage by travel time to COVID-19 vaccination sites in Kenya and examined how travel time was associated with perceived vaccine availability and vaccine uptake. Methods: We conducted a secondary analysis of cross-sectional data from Round 3 (October-December 2021) of the COVID-19 Gendered Risks, Impact, and Response Survey. Vaccination site locations were obtained from the Kenyan Ministry of Health. Travel times from constituency population-weighted centroids to the nearest site were estimated using a cost-path distance algorithm in AccessMod. Two binary outcomes were analysed: perceived vaccine availability and vaccine uptake. Logistic regression with restricted cubic splines modelled associations with travel time in the full sample and a restricted sample excluding respondents who reported vaccines as 'unavailable' despite modelled travel time <15 minutes. Results: Nationally, 55% of the population lived within 30 minutes of a vaccination site, 69% within 60 minutes, and 21.6% more than 120 minutes away, with shorter travel times concentrated around major cities and longer times in eastern and northern Kenya. In the full analytic sample, travel time was not significantly associated with perceived availability or uptake. In the restricted sample, longer travel time showed strong, nonlinear associations with lower perceived availability and uptake. Conclusions: Geographic barriers, captured by modelled travel time, are important determinants of perceived vaccine availability and uptake in Kenya. Combining high-resolution geospatial modelling with individual-level data can guide expansion of vaccination services to improve vaccine equity and preparedness for future health emergencies in Kenya and similar LMICs.

Indexed as

COVID-19COVID-19 VaccinesHealth Services AccessibilityTravelVaccinationVaccination CoverageAdultCross-Sectional StudiesFemaleHumansKenyaMaleMiddle AgedSpatial AnalysisTime FactorsCOVID-19 Vaccines

Identifiers

PMID41960709
PMCPMC13067299

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.