• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


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


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


  1. Gogoi K, Barman MP, Hazarika J. Indirect estimation of infant mortality rate in Assam:a district level analysis. Advances in Mathematics: Scientific Journal. 2021;10(3):1675–1689. Available from: https://doi.org/10.37418/amsj.10.3.50
  2. Woldeamanuel BT, Aga MA. Count Models Analysis of Factors Associated with Under-Five Mortality in Ethiopia. Global Pediatric Health. 2021;8. Available from: https://doi.org/10.1177/2333794X21989538
  3. Mulugeta SS, Muluneh MW, Belay AT, Moyehodie YA, Agegn SB, Masresha BM, et al. Multilevel log linear model to estimate the risk factors associated with infant mortality in Ethiopia: further analysis of 2016 EDHS. BMC Pregnancy and Childbirth. 2022;22(1):1. Available from: https://doi.org/10.1186/s12884-022-04868-9
  4. Darnah, Utoyo MI, Chamidah N. Modeling of Maternal Mortality and Infant Mortality Cases in East Kalimantan using Poisson Regression Approach Based on Local Linear Estimator. IOP Conference Series: Earth and Environmental Science. 2019;243(1):012023. Available from: https;//doi.org/10.1088/1755-1315/243/1/012023
  5. Kumar M, Shivgotra VK. Effect of seasonal variations on infant mortality rate of some selected districts of Jammu Division. Indian Journal of Community Medicine. 2020;45(4):415. Available from: https://doi.org/10.4103/ijcm.IJCM_441_19
  6. Prahutama A, Suparti, Munawaroh DA, Utami TW. Modeling bivariate Poisson regression for maternal and infant mortality in Central Java. AIP Conference Proceedings . 2021;2329. Available from: https://doi.org/10.1063/5.0042142
  7. Aragaw AM, Azene AG, Workie MS. Poisson logit hurdle model with associated factors of perinatal mortality in Ethiopia. Journal of Big Data. 2022;9(1):1. Available from: https://doi.org/10.1186/s40537-022-00567-6
  8. Nahm FS. Receiver operating characteristic curve: overview and practical use for clinicians. Korean Journal of Anesthesiology. 2022;75(1):25–36. Available from: https://doi.org/10.4097/kja.21209
  9. Odetunmibi OA, Adejumo AO, Anake TA. A study of Hepatitis B virus infection using chi-square statistic. Journal of Physics: Conference Series. 2021;1734(1):012010. Available from: https://doi.org/10.1088/1742-6596/1734/1/012010
  10. Dandapat B, Biswas S, Patra B. Religion, nutrition and birth weight among currently married women (15–49) in India: A study based on NFHS-5. Clinical Epidemiology and Global Health. 2023;20:101218. Available from: https://doi.org/10.1016/j.cegh.2023.101218
  11. Gebreegziabher E, Bountogo M, Sié A, Zakane A, Compaoré G, Ouedraogo T, et al. Influence of maternal age on birth and infant outcomes at 6 months: a cohort study with quantitative bias analysis. International Journal of Epidemiology. 2023;52(2):414–425. Available from: https://doi.org/10.1093/ije/dyac236
  12. Marphatia AA, Saville NM, Amable GS, Manandhar DS, Cortina-Borja M, Wells JC, et al. How Much Education Is Needed to Delay Women's Age at Marriage and First Pregnancy? Frontiers in Public Health. 2009;7:396. Available from: https://doi.org/10.3389/fpubh.2019.00396
  13. Ratiu D, Sauter F, Gilman E, Ludwig S, Ratiu J, Mallmann-Gottschalk N, et al. Impact of Advanced Maternal Age on Maternal and Neonatal Outcomes. In Vivo. 2023;37(4):1694–1702. Available from: https://doi.org/10.21873/invivo.13256
  14. Mfateneza E, Rutayisire PC, Biracyaza E, Musafiri S, Mpabuka WG. Application of machine learning methods for predicting infant mortality in Rwanda: analysis of Rwanda demographic health survey 2014–15 dataset. BMC Pregnancy and Childbirth. 2022;22(1):388. Available from: https://doi.org/10.1186/s12884-022-04699-8
  15. Chilot D, Belay DG, Ferede TA, Shitu K, Asratie MH, Ambachew S, et al. Pooled prevalence and determinants of antenatal care visits in countries with high maternal mortality: A multi-country analysis. Frontiers in Public Health. 2023;11. Available from: https://doi.org/10.3389/fpubh.2023.1035759


© 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)


Subscribe now for latest articles and news.