Infant mortality has generally been increasing and has become an issue that urgently needs to be addressed. As the number of infant deaths is count data, a Poisson regression model is needed to determine the causal factors. However, the assumption of equidispersion in Poisson regression is rarely satisfied. The overdispersion issue is frequently found in real data. Thus, this research employs mixed Poisson distribution modeling to overcome the overdispersion issue, namely, the inverse Gaussian Poisson regression (IGPR) model. In this study, a simple IGPR model, a modified IGPR model, and the negative binomial regression (NBR) model are compared. The results show that the modified IGPR model and the NBR model with an exposure variable outperform the benchmark, based on the global deviance and Akaike Information Criteria (AIC) value, to model the number of infant deaths in East Nusa Tenggara, Indonesia. The significant predictors that affect the number of infant mortalities are the percentage of complete basic immunization, the percentage of low birth weight (LBW), the percentage of babies under six months who receive exclusive breastfeeding, the percentage of infants who receive vitamin A, and the percentage of births assisted by health workers in the district.

Original languageEnglish
Title of host publicationSoft Computing in Data Science - 6th International Conference, SCDS 2021, Proceedings
EditorsAzlinah Mohamed, Bee Wah Yap, Jasni Mohamad Zain, Michael W. Berry
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9789811673337
Publication statusPublished - 2021
Event6th International Conference on Soft Computing in Data Science, SCDS 2021 - Virtual, Online
Duration: 2 Nov 20213 Nov 2021

Publication series

NameCommunications in Computer and Information Science
Volume1489 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference6th International Conference on Soft Computing in Data Science, SCDS 2021
CityVirtual, Online


  • Exposure
  • Infant mortalities
  • Negative binomial
  • Overdispersion
  • Poisson Inverse Gaussian


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