Geospatial approaches for mapping zero-dose children in low- and lower-1 middle-income countries: A scoping review

Background: Zero-dose (ZD) children remain a critical public health concern, particularly in low- and lower-middle-income countries (LLMICs), where over 80% of the global ZD population resides, disproportionately concentrated among the most marginalised. Geospatial methods have emerged as effective tools for identifying and targeting immunization gaps. However, no review has systematically documented the spatial data and methods used to identify and characterize ZD children and corresponding gaps. This scoping review addresses that gap across LLMICs. Methods: Following PRISMA-ScR guidelines, we searched for peer reviewed articles published upto 2025 on spatial modelling of childhood vaccination coverage in LLMICs using six databases: PubMed, Web of Science, Scopus, Cochrane, Embase, and EBSCOhost-CINAHL. We extracted details on study characteristics, covariate types and sources, modelling methods, and the gaps. Articles were thematically summarized focusing on geospatial data, modelling approaches, and their corresponding gaps. Results: Of 15,587 articles retrieved, 102 from 68 LLMICs were included, with 70% published between 2021 and 2024, and 87% concentrated in Ethiopia, Nigeria, and India. Only a fifth assessed ZD prevalence, based on two distinct definitions. Studies relied predominantly on household survey data, with routine administrative data underused. Covariate data were dominated by demographic factors (49%) with limited representation of hard-to-reach contexts. Methods included clustering and autocorrelation analysis (54%), spatial interpolation (45%), small-area estimation (13%), and machine learning (8%). Key gaps included inconsistent ZD definitions, missing covariates, data inaccuracies, sparse samples, and weak representation of conflict-affected, informal-settlement, and mobile populations. Conclusions: Despite growing availability of spatial data and methods, geospatial identification of ZD children remains concentrated in few countries, relies heavily on survey data, uses inconsistent definitions, and is constrained by limitations that systematically exclude the most marginalised populations. Addressing these gaps will require harmonised definitions, integrated data systems, and reproducible modelling approaches underpinned by sustained investment in local analytical capacity.