RESEARCH ARTICLE
Resampled Cox Proportional Hazards Models for Infant Mortality at the Kigali University Teaching Hospital
Paul Gatabazi*, Sileshi Fanta Melesse, Shaun Ramroop
Article Information
Identifiers and Pagination:
Year: 2019Volume: 12
First Page: 136
Last Page: 144
Publisher ID: TOPHJ-12-136
DOI: 10.2174/1874944501912010136
Article History:
Received Date: 21/10/2018Revision Received Date: 06/03/2019
Acceptance Date: 18/03/2019
Electronic publication date: 16/04/2019
Collection year: 2019
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.
Abstract
Introduction:
Resampling technique as a way of overcoming instability in Cox Proportional hazard model is used for measuring the risk and related standard error for the infant mortality, given socio-economic and clinical covariates for mother and children at the Kigali University Teaching Hospital in Rwanda.
Methods:
Bootstrap and jackknife Cox proportional hazards models was applied to N=2117 newborn data collected in 2016 at the Kigali University Teaching Hospital in Rwanda.
Results:
The unadjusted models revealed significance of the age of female parents, information on previous abortion, gender of a newborn, number of newborns at a time, APGAR, the weight of a newborn and the circumference of the head of a newborn.
Conclusion:
Statistical analysis supports two major findings: 1) parents under 20 years of age indicate a relatively higher risk of infant death, and 2) abnormality in the newborn's head and weight indicates a relatively higher risk of infant mortality. Recommendations include avoidance of pregnancy until after age 20 and clinically recommended nutrition for the mother during pregnancy to decrease the risk of infant mortality.