Geospatial analysis of immunisation outcomes in Nigeria using a Bayesian geostatistical approach and an interactive dashboard

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Background Nigeria’s significant contribution to the global pool of zero-dose children persists despite ongoing immunisation investments. This coverage deficits in Kano and Lagos states serve as stark indicators of underlying structural and socio-economic obstacles hindering equitable immunisation access at the sub-national level. Methods To investigate these barriers, localised secondary data from various sources were collected including data from the State Routine Immunisation (RI) microplans, demographic and health survey data, supportive supervision data, immunisation coverage survey data, administrative data from health management information systems (HMIS), and vaccine security and logistics data to study the structural determinants of zero dose children and missed communities. This data was then visualised in an interactive geospatial dashboard deployed in Tableau to visualise and interpret key immunisation metrics. Using the Stochastic Partial Differential Equation (SPDE) approach in R Interface to Integrated Nested Laplace Approximations (R-INLA), a geostatistical model was developed to predict immunisation outcomes based on structural determinants identified from the literature. Results Findings show that in Kano state, urban health facilities are 39% (OR = 0.609, 95% CI: 0.406–0.906) less likely to have zero-dose children compared to rural ones with less significant impact of the type of settlement on under-immunisation (OR = 1.146. 95% CI: 0.724–1.869). In Lagos state, the distinction between urban and rural settings does not significantly impact zero-dose (OR = 2.117, 95% CI:0.473–10.001) or under-immunisation rates (OR = 1.136, 95% CI:0.300–4.722). Conclusion Localised triangulation analysis of data from the two states demonstrates complex interaction between different structural determinants of immunisation outcomes. These findings emphasise the need to prioritise localised analysis of available data and invest in capacity for data analysis at lower levels of the health system, to enable the use of available data for decision-making and action.

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