Exploring district-level geographical variance of complete immunization coverage in India. (30th September 2020)

• Record Type: Journal Article
• Authors: Biswas, M
• Is Part Of: European journal of public health. Volume 30(2020)Supplement 5
• Publication Date: 2020-09-30
• Subjects: Epidemiology -- Europe -- Periodicals; Public health -- Europe -- Periodicals; 362.109405
• DOI: 10.1093/eurpub/ckaa166.956
• Languages: English
• ISSNs: 1101-1262

Abstract

Abstract: India has been significantly progressed in full immunization care over the last few decades. Existing literature has not been unaddressed the potential spatial variations in relationships between full immunization coverage and its influence on socio-economic factors. The study aims to explore place-specific spatial dependencies and heterogeneities in the relationships of various factors on the district-level full immunization coverage in India. The study used a geocoded database for 640 districts of India, drawn from the 4th wave of the National Family Health Survey (NFHS) in 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. Multivariate Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models were employed to examine spatial relationships and decrypt location-based district-level analysis. The prevalence of full immunization care was 62 percent as per the national figure. The GWR results revealed that the relationships between the outcome and set of cofactors were significantly place-specific and spatially clustering in terms of their respective magnitude, direction, and differences in due to local characteristics across India. In terms of model performance and prediction accuracy, the GWR model was performing better over OLS estimates through comparisons of R2 and Akaike Information Criterion (AICC) in both models. Furthermore, the GWR model also improves the reliabilities of the relationships by reducing spatial autocorrelations. The findings suggest that the local GWR model has the potential to explain complexities in place-specific variations that could be ignored by OLS on the local causes of immunization coverage. Thus, appropriate intervention should be devised to safeguard child from vaccine-preventable diseases reduce the geographical heterogeneity of full immunization coverage across India. Key messages: This study aims to explore place-specific spatial dependencies and heterogeneities in the relationships of various factors on the district-level full immunization coverage in India. To explain local complexities in place-specific variations that could be ignored by the traditional model on the local causes of full immunization coverage across India.