Analyzing Low Birth Weight in Gombe State: Insights from Generalized Linear Models with Covariates

Abstract

The issue of low birth weight (LBW) is a critical concern due to its significant impact on newborn health and development, affecting both immediate and long-term outcomes. This study employs Generalized Linear Models (GLM) to investigate the factors contributing to LBW, utilizing a comprehensive dataset of 2164 birth records from selected Primary Health Care centers in Gombe metropolis, Gombe State. The analysis highlights that maternal age and parity are key determinants of birth weight, with the interaction between these variables proving to be a substantial factor. Specifically, our results indicate that maternal age and the number of previous births significantly influence birth weight, while the effect of the baby's gender is relatively minor. We evaluated the performance of eight different models through deviance analysis, demonstrating that the optimal model incorporates both maternal age, parity, and their interaction. These findings emphasize the importance of considering both maternal age and parity in predicting birth weight. Although the study provides valuable insights, it also has limitations that suggest the need for further research to explore additional influencing factors and refine current models. Addressing these limitations could enhance strategies for improving newborn health outcomes.