2025-04-19 –, Room 130
Health disparities remain a critical challenge in public health, demanding innovative approaches to uncover inequities and drive actionable change. This webinar will demonstrate how Python can serve as a powerful tool for creating data visualizations that illustrate the unequal burden of HIV across different populations. Participants will learn how Python’s popular libraries, such as Matplotlib, Seaborn, and Plotly, can transform complex datasets into accessible, impactful visuals.
Using an HIV dataset containing demographic, geographic, and clinical variables, this session will guide attendees through a series of practical examples. From creating heatmaps and geospatial maps to analyzing temporal trends, the webinar emphasizes how to identify and communicate key social determinants related to race, gender, socioeconomic status, and access to care. Through hands-on demonstrations, attendees will see how Python’s capabilities streamline data analysis and visualization workflows.
Key takeaways from the session include identifying regions and communities in Texas, disproportionately affected by HIV, uncovering intersectional factors influencing health outcomes, and leveraging visual tools to inform policy and resource allocation. Special attention will be given to designing visuals that resonate with non-technical audiences, ensuring findings are actionable for public health professionals and policymakers.
Description: Data Viz in Python as a Tool to Study Health Disparities
Targeted to the intermediate Python user, this session will begin with a brief overview of the tools and libraries that will be used, such as Pandas, Matplotlib, Seaborn, Plotly, and GeoPandas. Participants will do hands-on coding, exploring how to transform secondary data into practical, professional visuals. Key coding topics include:
1. Data Preprocessing and Exploration:
o Advanced techniques in Pandas for cleaning and reshaping datasets, including handling missing data and filtering key variables.
o Conducting exploratory data analysis (EDA) to uncover trends and patterns related to HIV disparities.
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Building Complex Visualizations:
o Heatmaps with Seaborn to visualize correlations between demographic factors and health outcomes.
o Geospatial maps using GeoPandas and Plotly to pinpoint regions with high HIV prevalence and disparities in care access.
o Bar plots, stacked charts, and histograms to analyze outcomes across intersectional demographics.
o Time series plots using Matplotlib and Seaborn to explore temporal changes in HIV rates and interventions. -
Next Steps:
o Share Findings with Stakeholders: Present the visualizations and key insights to relevant stakeholders, such as public health officials, policymakers, healthcare providers, and community organizations, using clear and actionable language.
o Develop Targeted Interventions: Use the insights from the analysis to design and propose interventions aimed at addressing identified disparities, such as community outreach programs, resource allocation strategies, or policy changes.
o Monitor and Evaluate Impact: Implement a plan to track the effectiveness of interventions using measurable outcomes, such as reductions in infection rates or improvements in access to care, and iterate on strategies based on the results.
o Build Collaborative Partnerships: Partner with community organizations, research institutions, and funding agencies to amplify efforts, secure resources, and ensure sustained action to address health disparities over time.
This session will emphasize practical, hands-on coding, and participants are encouraged to follow along to develop scripts they can apply to their own datasets. By the end of the webinar, attendees will have a deeper understanding of how to use Python for data visualization and actionable insights in public health.
Previous knowledge expected
With over 10 years of real world data (RWD) experience in Informatics, Biostatistics, Data Science, and Epidemiology, and over 20 years as a Scientist, Dr. Kimberly Deas is a currently a Principal Analytics Research Scientist Consultant. Her work experiences and specializations include healthcare informatics, health disparities, chemical and cancer informatics, and computational toxicology. Dr. Deas is a passionate Data educator, teaching data science, healthcare analytics, and data visualization at the collegiate level primarily through coding webinars. In her spare time, Dr. Deas enjoys golf, crocheting, walking, and reading for leisure.