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Exploring Geographical Variance of Complete Immunization Coverage in India: A District-level Spatial Modelling Approach

Monirujjaman Biswas, Jawaharlal Nehru University

This study aimed to explore place-specific spatial dependencies and heterogeneities in the relationships of various factors on the district-level full immunization coverage for the 640 districts of India using the 4th wave of the National Family Health Survey, 2015–16. Univariate Moran’s I and LISA maps were used to confirm the spatial autocorrelation and geographical hotspots of the district-level full immunization coverage. Further, Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models were employed to examine spatial relationships and to decrypt location-based district-level analysis. The GWR results revealed the relationships between outcome and set of cofactors were significantly place-specific and spatially clustering in terms of their respective magnitude, direction, and differences due to local characteristics across India. Concerning model performance accuracy, the GWR model was performing better and best-fit over OLS estimates through comparisons of R2 and Akaike Information Criterion (AIC). The findings highlighted the geographical dependencies and heterogeneities in full immunization coverage across Indian districts were strongly explained by a multitude of factors, which can help policymakers in designing and implementing nationwide effective programmatic interventions to improve the coverage of full immunization care across India.

Keywords: Health and morbidity, Geo-referenced/geo-coded data, Spatial analysis/regression, Spatial dependence/heterogeneity

See extended abstract.

  Presented in Session P19.