Prediction of blood lead level in maternal and fetal us-ing generalized linear model

Abstract

Over the past decades, with advanced data collection techniques, a different type of data continues to appear in various biological, sciences, medical, social, and economical studies. Statistical modeling is essential in many scientific research areas because it explains the relationship between the response variable of interest and a number of explanatory variables. Generalized linear models (GLMs) are generalization of the linear regression models, which allow fitting regression models to response variable that is non normal and follows a general exponential family. The aim of this study is to encourage and initiate the application of GLMs to predict the maternal and fetal blood-lead level. The inverse Gaussian distribution with inverse quadratic link function is considered. Four main effects were significant in the prediction of the maternal blood-lead level (pica, smoking of mother, dairy products intake of mother, calcium intake of mother), while in the prediction of the fetal blood-lead level, two main effects showed significance (dairy products intake of mother and hemoglobin of mother).