{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T04:45:41Z","timestamp":1781671541847,"version":"3.54.5"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:00:00Z","timestamp":1767312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>By mid-2024, &amp;gt;13 billion COVID-19 vaccine doses had been administered globally, with totals continuing to rise into 2025, yet persistent inequities remain in low- and middle-income countries (LMICs). We examined spatial determinants of COVID-19 vaccination uptake (proportion of eligible persons vaccinated) in Kenya by using the most recent nationally representative survey, the Kenya Demographic and Health Survey 2022. Our central contribution is the detection of seven spatially concentrated vulnerability clusters, complemented by using a Development Index (DI) and equity auditing to guide targeted action.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We integrated socioeconomic, healthcare, environmental, and demographic measures at the Demographic and Health Survey cluster level; quantified spatial dependence (Moran\u2019s I; spatial lag models); identified socio-geographic clusters (K-means); estimated variable importance (random forest); and synthesized a DI. Equity was assessed by using the Erreygers Concentration Index (ECI) along two axes: wealth-based (poorest\u2192richest) and immunization-linked (lowest\u2192highest routine child immunization coverage).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our results reveal stark geographic disparities: vaccination rates range from 5.93% in Garissa to 46.02% in Nyeri, with urban clusters achieving significantly higher uptake. Key predictors include bank access (financial inclusion), household crowding, and environmental factors (nitrogen dioxide levels, precipitation). The DI correlated positively with uptake and the ECI indicated modest immunization-linked inequality and more pronounced wealth-related inequality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study underscores the need for targeted interventions, including mobile vaccination units, financial inclusion programs (e.g. M-Pesa subsidies), and the integration of COVID-19 vaccines into routine immunization programs. As Kenya and many LMICs integrate COVID-19 vaccination into routine immunization, our spatial approach, combining DI, cluster detection, and equity metrics, provides an operational toolkit to prioritize underserved areas, inform the placement of service points\/mobile teams, and monitor equity as programs transition from campaigns to routine delivery.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ije\/dyaf208","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T12:50:09Z","timestamp":1764679809000},"source":"Crossref","is-referenced-by-count":1,"title":["Spatial inequities in COVID-19 vaccination coverage across Kenya: a geospatial analysis of structural determinants and Development Index patterns"],"prefix":"10.1093","volume":"55","author":[{"given":"Larry","family":"Ni\u00f1o","sequence":"first","affiliation":[{"name":"Digital Epidemiology Laboratory, Digital Futures, University of Cincinnati , Cincinnati, OH,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agnes","family":"Kiragga","sequence":"additional","affiliation":[{"name":"African Population and Health Research Centre (APHRC) , Nairobi,","place":["Kenya"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"F Dewolfe","family":"Miller","sequence":"additional","affiliation":[{"name":"Department of Tropical Medicine and Medical Microbiology and Pharmacology, University of Hawaii at Manoa , Honolulu, HI,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel M","family":"Mwalili","sequence":"additional","affiliation":[{"name":"Strathmore University Department of Mathematical Sciences, , Nairobi,","place":["Kenya"]},{"name":"Jomo Kenyatta University of Agriculture and Technology , Nairobi,","place":["Kenya"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Godfrey","family":"Musuka","sequence":"additional","affiliation":[{"name":"International Initiative for Impact Evaluation , Harare,","place":["Zimbabwe"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diego F","family":"Cuadros","sequence":"additional","affiliation":[{"name":"Digital Epidemiology Laboratory, Digital Futures, University of Cincinnati , Cincinnati, OH,","place":["United States"]},{"name":"Department of Biological Sciences, University of Cincinnati , Cincinnati, OH,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"2026010209333160800_dyaf208-B1","volume-title":"The impact of the global COVID-19 vaccination campaign on all-cause mortality","author":"Agrawal V, Sood N, Whaley CM"},{"key":"2026010209333160800_dyaf208-B2","author":"Total COVID-19 vaccine doses administered"},{"key":"2026010209333160800_dyaf208-B3","volume-title":"ICF. 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