Analysis of Infant Congenital Malformation Data using the Bayesian Count Regression

Abstract

Background: Congenital malformations are one of the most important and common types of anomalies in infants, which are one of the main causes of disability and mortality in children. Objectives: This study aimed to investigate the risk factors affecting the incidence of congenital malformations, as well as the number of different infant anomalies recorded in neonatal health data in Khoy, Iran, during 2017. Methods: In this study, all neonates born in the maternity wards of hospitals in Khoy, Iran, during 2017 were evaluated in terms of gender, weight, and parental consanguinity. Hurdle and Zero-inflation approaches were utilized for the double Poisson model. Moreover, the data were collected using some checklists, and the analyses were performed in R-3-6-1 software. Result: According to the results of the present study, the Hurdle approach was better than Zero-inflation. The birth weight and parental consanguinity affected the incidence of congenital malformations in infants. Conclusion: Given that a significant proportion of infants are born without any congenital malformations, it is important to use count regression models based on excess zero approaches to assess congenital malformations. It is also necessary to take steps to reduce consanguineous marriages and the number of infants with low-birth-weight to prevent congenital malformations.