TY - GEN
T1 - A Modified Inverse Gaussian Poisson Regression with an Exposure Variable to Model Infant Mortality
AU - Mardalena, Selvi
AU - Purhadi,
AU - Purnomo, Jerry Dwi Trijoyo
AU - Prastyo, Dedy Dwi
N1 - Publisher Copyright:
© 2021, Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Exposure
KW - Infant mortalities
KW - Negative binomial
KW - Overdispersion
KW - Poisson Inverse Gaussian
UR - http://www.scopus.com/inward/record.url?scp=85119411857&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-7334-4_21
DO - 10.1007/978-981-16-7334-4_21
M3 - Conference contribution
AN - SCOPUS:85119411857
SN - 9789811673337
T3 - Communications in Computer and Information Science
SP - 286
EP - 300
BT - Soft Computing in Data Science - 6th International Conference, SCDS 2021, Proceedings
A2 - Mohamed, Azlinah
A2 - Yap, Bee Wah
A2 - Zain, Jasni Mohamad
A2 - Berry, Michael W.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Conference on Soft Computing in Data Science, SCDS 2021
Y2 - 2 November 2021 through 3 November 2021
ER -
