RESEARCH ARTICLE


Multiple Events Model for the Infant Mortality at Kigali University Teaching Hospital



P. Gatabazi*, S. F. Melesse, S. Ramroop
School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Private Bag X01 Scottsville - 3209 South Africa


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Creative Commons License
© 2018 Gatabazi et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Private Bag X01 Scottsville - 3209 South Africa; Tel: +27710513309; E-mail: gatabazi001@gmail.com


Abstract

Introduction:

The present study applies multiple events survival analysis to infant mortality at the Kigali University Teaching Hospital (KUTH) in Rwanda.

Materials and Methods:

The primary dataset consists of newborns from KUTH recorded in the year 2016 and in the current paper, a complete case analysis was used. Two events per subject were modeled namely death and the occurrence of at least one of the following conditions that may also cause long-term death to infants such as severe oliguria, severe prematurity, very low birth weight, macrosomia, severe respiratory distress, gastroparesis, hemolytic, trisomy, asphyxia and laparoschisis. Covariates of interest include demographic covariates namely the age and the place of residence for parents; clinical covariates for parents include obstetric antecedents, type of childbirth and previous abortion. Clinical covariates for babies include APGAR, gender, number of births at a time, weight, circumference of the head, and height.

Results/Conclusion:

The results revealed that Wei, Lin and Weissfeld Model (WLWM) fit the data well. The covariates age, abortion, gender, number, APGAR, weight and head were found to have a significant effect.

Keywords: Survival analysis, Multiple events, Rate function, Mean function, Intensity process, Infant mortality.