• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology

Article

Indian Journal of Science and Technology

Year: 2023, Volume: 16, Issue: 40, Pages: 3538-3548

Original Article

Determinants of Infant Mortality in Assam, India: Modelling with Generalised Linear Regression Model for Count Data

Received Date:03 August 2023, Accepted Date:28 September 2023, Published Date:28 October 2023

Abstract

Objectives: Compared to other states of the country, infant mortality situation in Assam is still substandard. This study attempts to model infant mortality counts, determine the associated risk factors in Assam. Methods: Data have been extracted from the NFHS-5 of India. The "number of infant deaths", is the explained variable used in the present study with several explanatory variables. 34,979 mothers in 33 districts of Assam are included to train the count model. The fitted models are compared by various techniques like Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC), Receiver Operating Characteristic (ROC) curves and Area under the curve (AUC). Findings: In the data set, 89.72% of the mothers have not faced any infant deaths in their lifetime. The average number of infant deaths is 0.39, with a variance of 0.689, indicating that overdispersion is present. However, the overdispersion could be due to observational variation or excess zeros. The likelihood ratio chi-square test is used to determine the significance of the inflation parameter. The result of the statistic is 312.32 with an associated P-value of 0.000, so the inflation parameter is significant. Hence, it is better to use a model that considers excess zeros. Among the models, the zero-inflated negative binomial model with the least AIC (34781.56) and BIC (35217.86) is considered to be a better-fitting model than the other candidate models to meet the objective. Moreover, the zero-inflated negative binomial model (ZINB) has the greatest AUC (0.7196). The regression analysis indicates that residential status, religion, wealth index, mother's age at first birth, mother's educational level, and birth order significantly influence the risk of infant mortality. Novelty: The findings of this study might help policymakers to identify which socio-economic and demographic groups should be given preference to encourage women and to reduce infant mortality.

Keywords: Count, Overdispersion, Infant Mortality, Assam, Zero Inflated Negative Binomial Regression

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Copyright

© 2023 Baruah et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)

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