{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T19:54:45Z","timestamp":1788292485027,"version":"build-2803163510"},"reference-count":95,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100001125","name":"Gavi","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100001125","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/0141yg674","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Vaccines"],"abstract":"<jats:p>Background: Spatial analysis methods, including model-based geostatistics (MBG), small-area estimation (SAE), and cluster detection, are increasingly used to map subnational immunisation coverage and identify geographic inequalities in low- and middle-income countries. However, the extent to which these methods capture the multidimensional determinants of immunisation uptake, and whether their outputs inform programme decisions in practice, remains unclear. Methods: We conducted a critical scoping review following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines, systematically searching PubMed and Google Scholar for studies applying spatial statistical methods to childhood immunisation coverage or equity. Findings were synthesised using a combination of descriptive summary and thematic and interpretive synthesis. Results: We included 50 studies from the 421 papers identified. Spatial methods have successfully revealed subnational coverage inequalities that national averages obscure, and studies developed in collaboration with national programme teams, integrating routine health system data alongside household surveys, produced the most operationally relevant outputs. However, most studies relied exclusively on survey data with a limited incorporation of supply-side determinants, and few discussed how uncertainty in estimates should constrain downstream use. Although a growing number of studies articulated clear implementation pathways, confirmed programmatic uptake of spatial outputs remained largely undocumented. The emergence of machine learning approaches (8 of 50 studies) offers predictive gains but introduces additional challenges around transparency and quality assurance for governance use. Conclusions: Spatial methods are becoming more frequently used for immunisation but are more likely to contribute to immunisation equity goals when co-produced with programme teams, matched to decision-relevant geographies, and accompanied by transparent documentation of model assumptions and limitations. Future research should prioritise quality frameworks for algorithm-assisted health estimates and systematic evaluation of whether spatial outputs improve decision-making relative to existing data sources.<\/jats:p>","DOI":"10.3390\/vaccines14070572","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:55:11Z","timestamp":1782780911000},"page":"572","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Assessing Geographic Inequalities in Childhood Immunisation Coverage: A Critical Scoping Review of Spatial Analysis Methods"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9663-7561","authenticated-orcid":false,"given":"Adrien","family":"Allorant","sequence":"first","affiliation":[{"name":"Department of Data, Digital Health, Analytics and AI, World Health Organization, CH-1211 Geneva, Switzerland"},{"name":"Department of Social Statistics and Demography, Statistical Sciences Research Institute (S3RI), University of Southampton, Southampton SO17 1BJ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8161-2599","authenticated-orcid":false,"given":"Nicole","family":"Bergen","sequence":"additional","affiliation":[{"name":"Department of Data, Digital Health, Analytics and AI, World Health Organization, CH-1211 Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7324-9198","authenticated-orcid":false,"given":"M. Carolina","family":"Danovaro-Holliday","sequence":"additional","affiliation":[{"name":"Department of Immunization, Vaccines and Biologicals, World Health Organization, CH-1211 Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6441-2555","authenticated-orcid":false,"given":"Joshua","family":"Lorin","sequence":"additional","affiliation":[{"name":"Gavi, the Vaccine Alliance, CH-1211 Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2125-9364","authenticated-orcid":false,"given":"Gustavo","family":"Caetano Corr\u00eaa","sequence":"additional","affiliation":[{"name":"Gavi, the Vaccine Alliance, CH-1211 Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danielle","family":"Boyda","sequence":"additional","affiliation":[{"name":"Gavi, the Vaccine Alliance, CH-1211 Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6954-6827","authenticated-orcid":false,"given":"Johanna","family":"Lee Belanger","sequence":"additional","affiliation":[{"name":"Department of Data, Digital Health, Analytics and AI, World Health Organization, CH-1211 Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ravi Shankar","family":"Santhana Gopala Krishnan","sequence":"additional","affiliation":[{"name":"Department of Data, Digital Health, Analytics and AI, World Health Organization, CH-1211 Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rocco","family":"Panciera","sequence":"additional","affiliation":[{"name":"Digital Health Transformation, Access and Equity Unit, UNICEF\u2014Nairobi Centre of Excellence, Nairobi 00100, Kenya"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7322-672X","authenticated-orcid":false,"given":"Ahmad Reza","family":"Hosseinpoor","sequence":"additional","affiliation":[{"name":"Department of Data, Digital Health, Analytics and AI, World Health Organization, CH-1211 Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2307","DOI":"10.1016\/S0140-6736(24)00850-X","article-title":"Contribution of vaccination to improved survival and health: Modelling 50 years of the Expanded Programme on Immunization","volume":"403","author":"Shattock","year":"2024","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"146","DOI":"10.2471\/BLT.19.241620","article-title":"Addressing the persistent inequities in immunization coverage","volume":"98","author":"Chopra","year":"2020","journal-title":"Bull. 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