Assessing the Prevalence and Risk Factors of Maternal Anemia: A Study on Knowledge Indicators and Nutritional Habits Among Pregnant Women

This presentation explores the prevalence of anemia and its associated factors among pregnant women attending antenatal care. Using a cross-sectional study design, data was collected from 297 pregnant women across three healthcare centers. The study utilized statistical analysis and machine learning models to identify key risk factors contributing to anemia, focusing on dietary habits, medical history, and socio-demographic variables. With more than 50% of the participants classified as anemic, the findings revealed significant correlations between anemia and factors such as pregnancy trimester, diet habits, and nutritional supplement usage. The study's results emphasize the need for targeted interventions to manage anemia during pregnancy, improving maternal and fetal health outcomes.

After attending this session, attendees will be able to explain the key risk factors associated with the development of anemia among pregnant women, particularly in low-income regions.

Attendees will be able to apply strategies for integrating health literacy and behavioral interventions to improve anemia prevention and management in clinical settings

Attendees will be able to distinguish the impact of educational level and specific health behaviors on anemia prevalence, enabling better-targeted public health interventions.

Authors

Omar Abueed is a second-year PhD student in Industrial and Systems Engineering at Binghamton University. His research primarily focuses on healthcare systems, with a particular emphasis on data analysis and medical image segmentation. He also works as a Graduate Project Assistant at Montefiore Care Management Center, applying Six Sigma methodologies to enhance hospital operations.
 

Dr. Yong Wang is a highly accomplished researcher specializing in various fields within engineering and systems science. With dual PhDs from the University of Illinois and Huazhong University of Science and Technology, Dr. Wang's expertise spans autonomous systems, energy systems, manufacturing systems, healthcare systems, operations research, and data science.