Making Sense of Data: Using Logistic Regression to Understand Neonatal Mortality

Understanding health data can be complex, but it doesn’t have to be.

In this short animated video, my team and I break down the results of a logistic regression model we developed. The model explores how antenatal care coverage(at least four visits,%) and the prevalence of anemia in pregnancy, %, and a country's economic class are associated with neonatal mortality rates.

📊 Through this project, we wanted to do more than run a regression—we wanted to make the data speak. So, we created this animation to translate the findings into a clear, engaging story about newborn survival around the world.

Short explainer video

🧠 What Does the Model Tell Us?

• Antenatal care attendance significantly reduces the odds of neonatal death.

• Anemia during pregnancy is a strong predictor of poor newborn outcomes.

• Country income level, while often assumed to be a driving factor, was less predictive in this model than clinical and care-related variables.

This analysis reminds us that even in global health, access to care and quality maternal health services can be stronger levers of change than broad economic labels.

This animation is part of my mission to make public health research more accessible—especially to communities, policymakers, and young professionals who are affected by these issues but might not read regression tables or academic journals. I believe storytelling, visual tools, and clear communication can turn data into action.

💬 Have questions or feedback? I’d love to hear from you in the comment section

📢 Share this video with someone who needs to see how data saves lives.