Hosted by Dean Ana Diez Roux (Dornsife School of Public Health) and Dean Laura Gitlin (College of Nursing and Health Professions), this virtual event will explore the complex topic of health inequities in cities, with a focus on the community Drexel calls home — Philadelphia. These respected experts will be joined by a panel of esteemed faculty and current students for a lively discussion focusing on how the COVID-19 pandemic has shown a spotlight on health inequities in urban areas and how the University is helping to combat increased disparities. The conversation will touch on the panelists' work related to access to care, community outreach, and education and how Drexel's dynamic academic programs prepare future leaders within the public health and health care fields.

Bayesian methods combine information from various sources and are increasingly used in biomedical and public health settings to accommodate complex data and produce readily interpretable output. This course will introduce students to Bayesian methods, emphasizing the basic methodological framework, real-world applications, and practical computing. Special consideration will be given to methods for spatial data analysis.

This course will provide the landscape of policy analysis in real-world settings using a continuum of data tool approaches. Since policy development and implementation occurs in diverse and fast-paced environments, approaches to studying research and policy questions that keep pace with policymakers will be described. Data approaches from individual case studies to large, publicly available data sets will be reviewed and used. Policy products (e.g., policy briefs, infographics) that resonate with policymakers and constituents will be the key deliverables generated and provide scaffolding for future policy analysis and dissemination.

Improving the quality of data visualizations can help in communicating results more clearly and transparently to other scientists, stakeholders and the general public. This course will use a workshop format to help students build their own data visualization projects using R, with Urban Health examples. This course will include a general introduction to basic data visualization concepts and best practices to generate static figures. This course will also cover basic concepts regarding interactive data visualization and spatial data. This course will provide all the required code, and some of the code may be useful to apply to your own visualizations with some adjustments. Thus, participants with a stronger R background will be able to create more complex visualizations. 

As data is collected from more and more sources and across life areas, the applications for using big data for public health purposes are growing. This class will focus on the use of large data sets, specifically claims data and other national survey sources, and provide an overview of structuring data, selecting optimal analytic approaches, and display results in compelling data visualization formats.