Binary Logistic Regression Methods for Modeling Broncho-Pneumonia Status in Infants from Tertiary Health Institutions in North Central Nigeria

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.