Speaker
Description
Background: Childhood immunization influences directly and indirectly fourteen out of the seventeen sustainable development goals (SDGs). Timely receipt of vaccines protects children from deadly diseases and increases the overall future productivity of the population. With the largest and most heterogeneous population of under-five children, the delay in receiving polio vaccination has not been properly explored using a conventional regression model in India. This study aimed to identify latent subgroups of children according to the polio vaccine timeliness and its associated risk factors.
Methods: Unit-level data of 102176, 106386, 98071, and 86773 children were used for four doses of Polio vaccine from fifth round of the National Family Health Survey (NFHS) in India. Latent subgroups and associated risk factors of four polio vaccine durations were obtained using C-point finite mixture negative binomial models.
Results: Among the children with available vaccination dates, about 51.86% received delayed a first dose. The second oral dose showed three latent classes (mean durations: 53, 81, and 127 days), whereas the third and fourth oral doses had three and four classes, respectively. Wealth, geographical zones, and maternal education significantly predicted class membership.
Discussion: This study is the first to explore the hidden patterns of vaccine delay and subgroup-specific risk factors using finite mixture model technique rather than the “one size fits all” approach, using the world’s largest household survey data. Different latent classes indicate distinct behavior for each dose, backing dose-specific policy intervention. Unveiling the underlying pattern in delay in receiving polio vaccine and its risk factors would help policymakers enable more targeted and efficient immunisation strategies. By identifying dose-specific hidden subgroup-wise patterns, this study provides an insight to assess progress towards SDG3.
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