Binary logistic regression methods for modeling broncho-pneumonia status in infants from tertiary health institutions in north central Nigeria

Y Yakubu,A Usman,I Audu,S.S Ahmed

doi:10.4314/jasem.v23i8.28

Abstract

Acute respiratory tract infections, predominantly bronchopneumonia, are one of the leading causes of infant deaths in developing countries and around the world. This work models the effects of the significant risk factors on infants’ bronchopneumonia status and also fits some reduced models and determines the best model with minimum number of parameters. The data for this study consist of a random sample of 433 births to women seen in the obstetrics clinic of two sampled tertiary health institutions in north-central Nigeria. These include University Teaching Hospital (UTH) Abuja, and Federal Medical Center (FMC) Keffi, Nasarawa State. Binary logistic regression was used to identify and model the effects of the various risk factors while stepwise regression technique was used to fit some reduced logistic regression models. Then the best fitting model with minimum number of parameters was identified using likelihood ratio statistic. It was observed that baby’s weight at birth, baby’s weight four weeks since birth, and mother’s occupation have significant effects on infant’s bronchopneumonia status. Additionally, among the four fitted reduced models, model4 is the best predictor of infants’ bronchopneumonia status, followed by model3 and then model2. Therefore, community service like home visiting for health education, supplementation of vitamin A, etc., would be an advantage if provided for teenaged pregnant women as it would, in turn, reduce incidence of low birth weight and thereby reduce bronchopneumonia infection among these children. Keywords: Bronchopneumonia, Multiple Logistic Regression Model, Fitness, likelihood ratio test

Highlights

Acute respiratory tract infection (ARI), predominantly pneumonia, is a major cause of morbidity and mortality among young children in developing countries
The fitted model was assessed for contribution of the individual factors and using stepwise regression technique, some reduced logistic regression models were fitted based on the variables with significant effects
Baby’s sex was entered into the model but its effect was insignificant and it was excluded. These results revealed that of all the factors considered in this study, baby’s weight after birth (BWABirth) is the best determinant of bronchopneumonia status in infants as it caused the highest reduction of 126.428 in the -2Log Likelihood statistic

Summary

Introduction

Acute respiratory tract infection (ARI), predominantly pneumonia, is a major cause of morbidity and mortality among young children in developing countries. The fitted model was assessed for contribution of the individual factors and using stepwise regression technique, some reduced logistic regression models were fitted based on the variables with significant effects These reduced models were compared for their goodness of fit and the best fitting model with minimum number of parameters, that is, the one that best predicts bronchopneumonia status in infants, was identified using likelihood ratio statistic. In most medical and epidemiologic studies, the outcome measure is categorical, such as occurrence or nonoccurrence of a disease, mortality (death or alive), etc., which may be coded as 1 or 0 Such studies call for evaluation of relative contribution of various factors to a single dichotomous or binary outcome variable and interest is always centered on modeling relationship between the probability of a success (which is between 0 and 1), and the explanatory variables (or risk factors). The work fits some reduced logistic regression models and determines the best model with minimum number of parameters

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Conclusion